From d913a585df5e5356d83a0036f0ec791601e56c75 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 15 Sep 2023 20:51:34 -0400 Subject: [PATCH 001/205] First version of the continuous lattice parameters environment as a continuous hypercube. --- config/env/crystals/clattice_parameters.yaml | 43 ++++ .../clatticeparams/clatticeparams_owl.yaml | 78 ++++++ gflownet/envs/crystals/clattice_parameters.py | 242 ++++++++++++++++++ .../gflownet/envs/test_clattice_parameters.py | 206 +++++++++++++++ 4 files changed, 569 insertions(+) create mode 100644 config/env/crystals/clattice_parameters.yaml create mode 100644 config/experiments/clatticeparams/clatticeparams_owl.yaml create mode 100644 gflownet/envs/crystals/clattice_parameters.py create mode 100644 tests/gflownet/envs/test_clattice_parameters.py diff --git a/config/env/crystals/clattice_parameters.yaml b/config/env/crystals/clattice_parameters.yaml new file mode 100644 index 000000000..084a44a6f --- /dev/null +++ b/config/env/crystals/clattice_parameters.yaml @@ -0,0 +1,43 @@ +defaults: + - base + +_target_: gflownet.envs.crystals.clattice_parameters.CLatticeParameters + +id: clattice_parameters +continuous: True + +# Lattice system +lattice_system: triclinic +# Allowed ranges of size and angles +min_length: 1.0 +max_length: 5.0 +min_angle: 30.0 +max_angle: 150.0 + +# Policy +beta_params_min: 0.01 +beta_params_max: 1000.0 +min_incr: 0.1 +n_comp: 1 +fixed_distribution: + beta_weights: 1.0 + beta_alpha: 0.01 + beta_beta: 0.01 + bernoulli_source_logit: 0.0 + bernoulli_eos_logit: 0.0 +random_distribution: + beta_weights: 1.0 + # IMPORTANT: adjust because of sigmoid! + beta_alpha: 0.01 + beta_beta: $beta_params_max + bernoulli_source_logit: 0.0 + bernoulli_eos_logit: 0.0 +# Buffer +buffer: + data_path: null + train: null + test: + type: grid + n: 1000 + output_csv: ccube_test.csv + output_pkl: ccube_test.pkl diff --git a/config/experiments/clatticeparams/clatticeparams_owl.yaml b/config/experiments/clatticeparams/clatticeparams_owl.yaml new file mode 100644 index 000000000..0b7183a8b --- /dev/null +++ b/config/experiments/clatticeparams/clatticeparams_owl.yaml @@ -0,0 +1,78 @@ +# @package _global_ + +defaults: + - override /env: ccube + - override /gflownet: trajectorybalance + - override /proxy: corners + - override /logger: wandb + - override /user: alex + +# Environment +env: + # Lattice system + lattice_system: cubic + # Allowed ranges of size and angles + min_length: 1.0 + max_length: 5.0 + min_angle: 30.0 + max_angle: 150.0 + # Cube + n_comp: 5 + beta_params_min: 0.01 + beta_params_max: 100.0 + min_incr: 0.1 + fixed_distribution: + beta_weights: 1.0 + beta_alpha: 0.01 + beta_beta: 0.01 + bernoulli_source_logit: 1.0 + bernoulli_eos_logit: 1.0 + random_distribution: + beta_weights: 1.0 + beta_alpha: 0.01 + beta_beta: 0.01 + bernoulli_source_logit: 1.0 + bernoulli_eos_logit: 1.0 + reward_func: identity + +# GFlowNet hyperparameters +gflownet: + random_action_prob: 0.1 + optimizer: + batch_size: + forward: 100 + lr: 0.0001 + z_dim: 16 + lr_z_mult: 100 + n_train_steps: 10000 + policy: + forward: + type: mlp + n_hid: 512 + n_layers: 5 + checkpoint: forward + backward: + type: mlp + n_hid: 512 + n_layers: 5 + shared_weights: False + checkpoint: backward + +# WandB +logger: + lightweight: True + project_name: "GFlowNet Cube" + tags: + - gflownet + - continuous + - ccube + test: + period: 500 + n: 1000 + checkpoints: + period: 500 + +# Hydra +hydra: + run: + dir: ${user.logdir.root}/debug/ccube/${now:%Y-%m-%d_%H-%M-%S} diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py new file mode 100644 index 000000000..3c4e75e3d --- /dev/null +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -0,0 +1,242 @@ +""" +Classes to represent continuous lattice parameters environments. +""" +from typing import List, Optional, Tuple + +import numpy as np +import torch +from torch import Tensor +from torchtyping import TensorType + +from gflownet.envs.crystals.lattice_parameters import LatticeParameters +from gflownet.envs.cube import ContinuousCube +from gflownet.utils.common import copy +from gflownet.utils.crystals.constants import ( + CUBIC, + HEXAGONAL, + LATTICE_SYSTEMS, + MONOCLINIC, + ORTHORHOMBIC, + RHOMBOHEDRAL, + TETRAGONAL, + TRICLINIC, +) + + +# TODO: figure out a way to inherit the (discrete) LatticeParameters env or create a +# common class for both discrete and continous with the common methods. +class CLatticeParameters(ContinuousCube): + """ + Continuous lattice parameters environment for crystal structures generation. + + Models lattice parameters (three edge lengths and three angles describing unit + cell) with the constraints given by the provided lattice system (see + https://en.wikipedia.org/wiki/Bravais_lattice). This is implemented by inheriting + from the (continuous) cube environment, creating a mapping between cell position + and edge length or angle, and imposing lattice system constraints on their values. + + Similar to the Cube environment, the values are initialized with zeros + (or target angles, if they are predetermined by the lattice system), and are + incremented by sampling from a (mixture of) Beta distribution(s). + + The values of the state will remain in the default [0, 1] range of the Cube, but + they are mapped to [min_length, max_length] in the case of the lengths and + [min_angle, max_angle] in the case of the angles. + """ + + def __init__( + self, + lattice_system: str, + min_length: float = 1.0, + max_length: float = 5.0, + min_angle: float = 30.0, + max_angle: float = 150.0, + **kwargs, + ): + """ + Args + ---- + lattice_system : str + One of the seven lattice systems. + + min_length : float + Minimum value of the lengths. + + max_length : float + Maximum value of the lengths. + + min_angle : float + Minimum value of the angles. + + max_angle : float + Maximum value of the angles. + """ + self.lengths = ("a", "b", "c") + self.angles = ("alpha", "beta", "gamma") + self.parameters = self.lengths + self.angles + self.lattice_system = lattice_system + self.min_length = min_length + self.max_length = max_length + self.length_range = self.max_length - self.min_length + self.min_angle = min_angle + self.max_angle = max_angle + self.angle_range = self.max_angle - self.min_angle + n_dim = self._setup_constraints() + super().__init__(n_dim=n_dim, **kwargs) + + def _statevalue2length(self, value): + return self.min_length + value * self.length_range + + def _length2statevalue(self, length): + return (length - self.min_length) / self.length_range + + def _statevalue2angle(self, value): + return self.min_angle + value * self.angle_range + + def _angle2statevalue(self, angle): + return (angle - self.min_angle) / self.angle_range + + def _get_param(self, param): + if hasattr(self, param): + return getattr(self, param) + else: + if param in self.lengths: + return self._statevalue2length( + self.state[self._get_index_of_param(param)] + ) + elif param in self.angles: + return self._statevalue2angle( + self.state[self._get_index_of_param(param)] + ) + else: + raise ValueError(f"{param} is not a valid lattice parameter") + + def _set_param(self, state, param, value): + param_idx = self._get_index_of_param(param) + if param_idx: + if param in self.lengths: + state[param_idx] = self._length2statevalue(value) + elif param in self.angles: + state[param_idx] = self._angle2statevalue(value) + else: + raise ValueError(f"{param} is not a valid lattice parameter") + return state + + def _get_index_of_param(self, param): + param_idx = f"{param}_idx" + if hasattr(self, param_idx): + return getattr(self, param_idx) + else: + return None + + def _setup_constraints(self): + """ + Computes the effective number of dimensions, given the constraints imposed by + the lattice system. + + Returns + ------- + n_dim : int + The number of effective dimensions that can be be udpated in the + environment, given the constraints set by the lattice system. + """ + # Lengths: a, b, c + n_dim = 0 + # a == b == c + if self.lattice_system in [CUBIC, RHOMBOHEDRAL]: + n_dim += 1 + self.a_idx = 0 + self.b_idx = 0 + self.c_idx = 0 + # a == b != c + elif self.lattice_system in [HEXAGONAL, TETRAGONAL]: + n_dim += 2 + self.a_idx = 0 + self.b_idx = 0 + self.c_idx = 1 + # a != b and a != c and b != c + elif self.lattice_system in [MONOCLINIC, ORTHORHOMBIC, TRICLINIC]: + n_dim += 3 + self.a_idx = 0 + self.b_idx = 1 + self.c_idx = 2 + else: + raise NotImplementedError + # Angles: alpha, beta, gamma + # alpha == beta == gamma == 90.0 + if self.lattice_system in [CUBIC, ORTHORHOMBIC, TETRAGONAL]: + self.alpha_idx = None + self.alpha = 90.0 + self.beta_idx = None + self.beta = 90.0 + self.gamma_idx = None + self.gamma = 90.0 + # alpha == beta == 90.0 and gamma == 120.0 + elif self.lattice_system == HEXAGONAL: + self.alpha_idx = None + self.alpha = 90.0 + self.beta_idx = None + self.beta = 90.0 + self.gamma_idx = None + self.gamma = 120.0 + # alpha == gamma == 90.0 and beta != 90.0 + elif self.lattice_system == MONOCLINIC: + n_dim += 1 + self.alpha_idx = None + self.alpha = 90.0 + self.beta_idx = n_dim - 1 + self.gamma_idx = None + self.gamma = 90.0 + # alpha == beta == gamma != 90.0 + elif self.lattice_system == RHOMBOHEDRAL: + n_dim += 1 + self.alpha_idx = n_dim - 1 + self.beta_idx = n_dim - 1 + self.gamma_idx = n_dim - 1 + # alpha != beta, alpha != gamma, beta != gamma + elif self.lattice_system == TRICLINIC: + n_dim += 3 + self.alpha_idx = 3 + self.beta_idx = 4 + self.gamma_idx = 5 + else: + raise NotImplementedError + return n_dim + + def _unpack_lengths_angles( + self, state: Optional[List[int]] = None + ) -> Tuple[Tuple, Tuple]: + """ + Helper that 1) unpacks values coding lengths and angles from the state or from + the attributes of the instance and 2) converts them to actual edge lengths and + angles. + """ + state = self._get_state(state) + + a, b, c, alpha, beta, gamma = [self._get_param(p) for p in self.parameters] + return (a, b, c), (alpha, beta, gamma) + + def state2readable(self, state: Optional[List[int]] = None) -> str: + """ + Converts the state into a human-readable string in the format "(a, b, c), + (alpha, beta, gamma)". + """ + state = self._get_state(state) + + lengths, angles = self._unpack_lengths_angles(state) + return f"{lengths}, {angles}" + + def readable2state(self, readable: str) -> List[int]: + """ + Converts a human-readable representation of a state into the standard format. + """ + state = copy(self.source) + + for c in ["(", ")", " "]: + readable = readable.replace(c, "") + values = readable.split(",") + values = [float(value) for value in values] + + for param, value in zip(self.parameters, values): + state = self._set_param(state, param, value) + return state diff --git a/tests/gflownet/envs/test_clattice_parameters.py b/tests/gflownet/envs/test_clattice_parameters.py new file mode 100644 index 000000000..7c5dfffb4 --- /dev/null +++ b/tests/gflownet/envs/test_clattice_parameters.py @@ -0,0 +1,206 @@ +import common +import pytest +import torch + +from gflownet.envs.crystals.clattice_parameters import ( + CUBIC, + HEXAGONAL, + LATTICE_SYSTEMS, + MONOCLINIC, + ORTHORHOMBIC, + RHOMBOHEDRAL, + TETRAGONAL, + TRICLINIC, + CLatticeParameters, +) + +N_REPETITIONS = 100 + + +@pytest.fixture() +def env(lattice_system): + return CLatticeParameters( + lattice_system=lattice_system, + min_length=1.0, + max_length=5.0, + min_angle=30.0, + max_angle=150.0, + ) + + +@pytest.mark.parametrize("lattice_system", LATTICE_SYSTEMS) +def test__environment__initializes_properly(env, lattice_system): + pass + + +@pytest.mark.parametrize( + "lattice_system, expected_params", + [ + (CUBIC, [1, 1, 1, 90, 90, 90]), + (HEXAGONAL, [1, 1, 1, 90, 90, 120]), + (MONOCLINIC, [1, 1, 1, 90, 30, 90]), + (ORTHORHOMBIC, [1, 1, 1, 90, 90, 90]), + (RHOMBOHEDRAL, [1, 1, 1, 30, 30, 30]), + (TETRAGONAL, [1, 1, 1, 90, 90, 90]), + (TRICLINIC, [1, 1, 1, 30, 30, 30]), + ], +) +def test__environment__has_expected_initial_parameters( + env, lattice_system, expected_params +): + (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert a == expected_params[0] + assert b == expected_params[1] + assert c == expected_params[2] + assert alpha == expected_params[3] + assert beta == expected_params[4] + assert gamma == expected_params[5] + + +@pytest.mark.parametrize( + "lattice_system", + [CUBIC], +) +@pytest.mark.repeat(N_REPETITIONS) +def test__cubic__constraints_remain_after_random_actions(env, lattice_system): + env = env.reset() + while not env.done: + (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert a == b + assert b == c + assert a == c + assert alpha == 90.0 + assert beta == 90.0 + assert gamma == 90.0 + env.step_random() + + +@pytest.mark.parametrize( + "lattice_system", + [HEXAGONAL], +) +@pytest.mark.repeat(N_REPETITIONS) +def test__hexagonal__constraints_remain_after_random_actions(env, lattice_system): + env = env.reset() + while not env.done: + (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert a == b + assert alpha == 90.0 + assert beta == 90.0 + assert gamma == 120.0 + env.step_random() + + +@pytest.mark.parametrize( + "lattice_system", + [MONOCLINIC], +) +@pytest.mark.repeat(N_REPETITIONS) +def test__monoclinic__constraints_remain_after_random_actions(env, lattice_system): + env = env.reset() + while not env.done: + (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert alpha == 90.0 + assert beta != 90.0 + assert gamma == 90.0 + env.step_random() + + +@pytest.mark.parametrize( + "lattice_system", + [ORTHORHOMBIC], +) +@pytest.mark.repeat(N_REPETITIONS) +def test__orthorhombic__constraints_remain_after_random_actions(env, lattice_system): + env = env.reset() + while not env.done: + (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert alpha == 90.0 + assert beta == 90.0 + assert gamma == 90.0 + env.step_random() + + +@pytest.mark.parametrize( + "lattice_system", + [RHOMBOHEDRAL], +) +@pytest.mark.repeat(N_REPETITIONS) +def test__rhombohedral__constraints_remain_after_random_actions(env, lattice_system): + env = env.reset() + while not env.done: + (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert a == b + assert b == c + assert a == c + assert alpha == beta + assert beta == gamma + assert alpha == gamma + assert alpha != 90.0 + env.step_random() + + +@pytest.mark.parametrize( + "lattice_system", + [TETRAGONAL], +) +@pytest.mark.repeat(N_REPETITIONS) +def test__tetragonal__constraints_remain_after_random_actions(env, lattice_system): + env = env.reset() + while not env.done: + (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert a == b + assert alpha == 90.0 + assert beta == 90.0 + assert gamma == 90.0 + env.step_random() + + +@pytest.mark.parametrize( + "lattice_system", + [TRICLINIC], +) +@pytest.mark.repeat(N_REPETITIONS) +def test__triclinic__constraints_remain_after_random_actions(env, lattice_system): + env = env.reset() + while not env.done: + # TODO: Test not equality constraints + env.step_random() + (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert len({alpha, beta, gamma, 90.0}) == 4 + + +@pytest.mark.parametrize( + "lattice_system, expected_output", + [ + (CUBIC, "(1.0, 1.0, 1.0), (90.0, 90.0, 90.0)"), + (HEXAGONAL, "(1.0, 1.0, 1.0), (90.0, 90.0, 120.0)"), + (MONOCLINIC, "(1.0, 1.0, 1.0), (90.0, 30.0, 90.0)"), + (ORTHORHOMBIC, "(1.0, 1.0, 1.0), (90.0, 90.0, 90.0)"), + (RHOMBOHEDRAL, "(1.0, 1.0, 1.0), (30.0, 30.0, 30.0)"), + (TETRAGONAL, "(1.0, 1.0, 1.0), (90.0, 90.0, 90.0)"), + (TRICLINIC, "(1.0, 1.0, 1.0), (30.0, 30.0, 30.0)"), + ], +) +def test__state2readable__gives_expected_results_for_initial_states( + env, lattice_system, expected_output +): + assert env.state2readable() == expected_output + + +@pytest.mark.parametrize( + "lattice_system, readable", + [ + (CUBIC, "(1.0, 1.0, 1.0), (90.0, 90.0, 90.0)"), + (HEXAGONAL, "(1.0, 1.0, 1.0), (90.0, 90.0, 120.0)"), + (MONOCLINIC, "(1.0, 1.0, 1.0), (90.0, 30.0, 90.0)"), + (ORTHORHOMBIC, "(1.0, 1.0, 1.0), (90.0, 90.0, 90.0)"), + (RHOMBOHEDRAL, "(1.0, 1.0, 1.0), (30.0, 30.0, 30.0)"), + (TETRAGONAL, "(1.0, 1.0, 1.0), (90.0, 90.0, 90.0)"), + (TRICLINIC, "(1.0, 1.0, 1.0), (30.0, 30.0, 30.0)"), + ], +) +def test__readable2state__returns_initial_state_for_rhombohedral_and_triclinic( + env, lattice_system, readable +): + assert env.readable2state(readable) == env.state From b8f18069e230f50412a00c7349852f96fd10d56a Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 15 Sep 2023 20:55:43 -0400 Subject: [PATCH 002/205] Add common tests for all lattice systems. --- tests/gflownet/envs/test_clattice_parameters.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/tests/gflownet/envs/test_clattice_parameters.py b/tests/gflownet/envs/test_clattice_parameters.py index 7c5dfffb4..17a9e8592 100644 --- a/tests/gflownet/envs/test_clattice_parameters.py +++ b/tests/gflownet/envs/test_clattice_parameters.py @@ -204,3 +204,11 @@ def test__readable2state__returns_initial_state_for_rhombohedral_and_triclinic( env, lattice_system, readable ): assert env.readable2state(readable) == env.state + + +@pytest.mark.parametrize( + "lattice_system", + [CUBIC, HEXAGONAL, MONOCLINIC, ORTHORHOMBIC, RHOMBOHEDRAL, TETRAGONAL, TRICLINIC], +) +def test__continuous_env_common(env, lattice_system): + return common.test__continuous_env_common(env) From 717a71626476e054f3facb6f32793170e7147dc6 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 16 Sep 2023 10:12:29 -0400 Subject: [PATCH 003/205] Fix experiment config file. --- config/experiments/clatticeparams/clatticeparams_owl.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/experiments/clatticeparams/clatticeparams_owl.yaml b/config/experiments/clatticeparams/clatticeparams_owl.yaml index 0b7183a8b..2f9c6ee0e 100644 --- a/config/experiments/clatticeparams/clatticeparams_owl.yaml +++ b/config/experiments/clatticeparams/clatticeparams_owl.yaml @@ -1,7 +1,7 @@ # @package _global_ defaults: - - override /env: ccube + - override /env: crystals/clattice_parameters - override /gflownet: trajectorybalance - override /proxy: corners - override /logger: wandb From ba2259a92e8621d170b29ae4f09d8866086d4577 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 16 Sep 2023 10:44:41 -0400 Subject: [PATCH 004/205] Add mask of ignored dimensions to forward and backward mask of cube. --- gflownet/envs/cube.py | 305 +++++++++++++++--------------- tests/gflownet/envs/test_ccube.py | 54 +++--- 2 files changed, 184 insertions(+), 175 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 0c160d221..b01e17359 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -1,42 +1,42 @@ """ -Classes to represent hyper-cube environments +classes to represent hyper-cube environments """ import itertools -from abc import ABC, abstractmethod -from typing import List, Optional, Tuple +from abc import abc, abstractmethod +from typing import list, optional, tuple import matplotlib.pyplot as plt import numpy as np import numpy.typing as npt import pandas as pd import torch -from sklearn.neighbors import KernelDensity -from torch.distributions import Bernoulli, Beta, Categorical, MixtureSameFamily, Uniform -from torchtyping import TensorType +from sklearn.neighbors import kerneldensity +from torch.distributions import bernoulli, beta, categorical, mixturesamefamily, uniform +from torchtyping import tensortype -from gflownet.envs.base import GFlowNetEnv +from gflownet.envs.base import gflownetenv from gflownet.utils.common import copy, tbool, tfloat -class Cube(GFlowNetEnv, ABC): +class cube(gflownetenv, abc): """ - Continuous (hybrid: discrete and continuous) hyper-cube environment (continuous + continuous (hybrid: discrete and continuous) hyper-cube environment (continuous version of a hyper-grid) in which the action space consists of the increment of dimension d, modelled by a beta distribution. - The states space is the value of each dimension. If the value of a dimension gets + the states space is the value of each dimension. if the value of a dimension gets larger than max_val, then the trajectory is ended. - Attributes + attributes ---------- n_dim : int - Dimensionality of the hyper-cube. + dimensionality of the hyper-cube. max_val : float - Max length of the hyper-cube. + max length of the hyper-cube. min_incr : float - Minimum increment in the actions, expressed as the fraction of max_val. This is + minimum increment in the actions, expressed as the fraction of max_val. this is necessary to ensure coverage of the state space. """ @@ -67,22 +67,22 @@ def __init__( assert n_dim > 0 assert max_val > 0.0 assert n_comp > 0 - # Main properties + # main properties self.n_dim = n_dim self.eos = self.n_dim self.max_val = max_val self.min_incr = min_incr * self.max_val - # Parameters of the policy distribution + # parameters of the policy distribution self.n_comp = n_comp self.beta_params_min = beta_params_min self.beta_params_max = beta_params_max - # Source state: position 0 at all dimensions + # source state: position 0 at all dimensions self.source = [0.0 for _ in range(self.n_dim)] - # Action from source: (n_dim, 0) + # action from source: (n_dim, 0) self.action_source = (self.n_dim, 0) - # End-of-sequence action: (n_dim + 1, 0) + # end-of-sequence action: (n_dim + 1, 0) self.eos = (self.n_dim + 1, 0) - # Conversions: only conversions to policy are implemented and the rest are the + # conversions: only conversions to policy are implemented and the rest are the # same self.state2proxy = self.state2policy self.statebatch2proxy = self.statebatch2policy @@ -90,194 +90,194 @@ def __init__( self.state2oracle = self.state2proxy self.statebatch2oracle = self.statebatch2proxy self.statetorch2oracle = self.statetorch2proxy - # Base class init + # base class init super().__init__( fixed_distr_params=fixed_distr_params, random_distr_params=random_distr_params, **kwargs, ) - self.continuous = True + self.continuous = true @abstractmethod def get_action_space(self): pass @abstractmethod - def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: + def get_policy_output(self, params: dict) -> tensortype["policy_output_dim"]: pass @abstractmethod def get_mask_invalid_actions_forward( self, - state: Optional[List] = None, - done: Optional[bool] = None, - ) -> List: + state: optional[list] = none, + done: optional[bool] = none, + ) -> list: pass @abstractmethod - def get_mask_invalid_actions_backward(self, state=None, done=None, parents_a=None): + def get_mask_invalid_actions_backward(self, state=none, done=none, parents_a=none): pass def statetorch2policy( - self, states: TensorType["batch", "state_dim"] = None - ) -> TensorType["batch", "policy_input_dim"]: + self, states: tensortype["batch", "state_dim"] = none + ) -> tensortype["batch", "policy_input_dim"]: """ - Clips the states into [0, max_val] and maps them to [-1.0, 1.0] + clips the states into [0, max_val] and maps them to [-1.0, 1.0] - Args + args ---- state : list - State + state """ return 2.0 * torch.clip(states, min=0.0, max=self.max_val) - 1.0 def statebatch2policy( - self, states: List[List] - ) -> TensorType["batch", "state_proxy_dim"]: + self, states: list[list] + ) -> tensortype["batch", "state_proxy_dim"]: """ - Clips the states into [0, max_val] and maps them to [-1.0, 1.0] + clips the states into [0, max_val] and maps them to [-1.0, 1.0] - Args + args ---- state : list - State + state """ return self.statetorch2policy( tfloat(states, device=self.device, float_type=self.float) ) - def state2policy(self, state: List = None) -> List: + def state2policy(self, state: list = none) -> list: """ - Clips the state into [0, max_val] and maps it to [-1.0, 1.0] + clips the state into [0, max_val] and maps it to [-1.0, 1.0] """ - if state is None: + if state is none: state = self.state.copy() return [2.0 * min(max(0.0, s), self.max_val) - 1.0 for s in state] - def state2readable(self, state: List) -> str: + def state2readable(self, state: list) -> str: """ - Converts a state (a list of positions) into a human-readable string + converts a state (a list of positions) into a human-readable string representing a state. """ return str(state).replace("(", "[").replace(")", "]").replace(",", "") - def readable2state(self, readable: str) -> List: + def readable2state(self, readable: str) -> list: """ - Converts a human-readable string representing a state into a state as a list of + converts a human-readable string representing a state into a state as a list of positions. """ return [el for el in readable.strip("[]").split(" ")] @abstractmethod def get_parents( - self, state: List = None, done: bool = None, action: Tuple[int, float] = None - ) -> Tuple[List[List], List[Tuple[int, float]]]: + self, state: list = none, done: bool = none, action: tuple[int, float] = none + ) -> tuple[list[list], list[tuple[int, float]]]: """ - Determines all parents and actions that lead to state. + determines all parents and actions that lead to state. - Args + args ---- state : list - Representation of a state + representation of a state done : bool - Whether the trajectory is done. If None, done is taken from instance. + whether the trajectory is done. if none, done is taken from instance. action : int - Last action performed + last action performed - Returns + returns ------- parents : list - List of parents in state format + list of parents in state format actions : list - List of actions that lead to state for each parent in parents + list of actions that lead to state for each parent in parents """ pass @abstractmethod def sample_actions_batch( self, - policy_outputs: TensorType["n_states", "policy_output_dim"], + policy_outputs: tensortype["n_states", "policy_output_dim"], sampling_method: str = "policy", - mask_invalid_actions: TensorType["n_states", "1"] = None, + mask_invalid_actions: tensortype["n_states", "1"] = none, temperature_logits: float = 1.0, loginf: float = 1000, - ) -> Tuple[List[Tuple], TensorType["n_states"]]: + ) -> tuple[list[tuple], tensortype["n_states"]]: """ - Samples a batch of actions from a batch of policy outputs. + samples a batch of actions from a batch of policy outputs. """ pass def get_logprobs( self, - policy_outputs: TensorType["n_states", "policy_output_dim"], + policy_outputs: tensortype["n_states", "policy_output_dim"], is_forward: bool, - actions: TensorType["n_states", 2], - mask_invalid_actions: TensorType["batch_size", "policy_output_dim"] = None, + actions: tensortype["n_states", 2], + mask_invalid_actions: tensortype["batch_size", "policy_output_dim"] = none, loginf: float = 1000, - ) -> TensorType["batch_size"]: + ) -> tensortype["batch_size"]: """ - Computes log probabilities of actions given policy outputs and actions. + computes log probabilities of actions given policy outputs and actions. """ pass def step( - self, action: Tuple[int, float] - ) -> Tuple[List[float], Tuple[int, float], bool]: + self, action: tuple[int, float] + ) -> tuple[list[float], tuple[int, float], bool]: """ - Executes step given an action. + executes step given an action. - Args + args ---- action : tuple - Action to be executed. An action is a tuple with two values: + action to be executed. an action is a tuple with two values: (dimension, increment). - Returns + returns ------- self.state : list - The sequence after executing the action + the sequence after executing the action action : int - Action executed + action executed valid : bool - False, if the action is not allowed for the current state, e.g. stop at the + false, if the action is not allowed for the current state, e.g. stop at the root state """ pass -class ContinuousCube(Cube): +class continuouscube(cube): """ - Continuous hyper-cube environment (continuous version of a hyper-grid) in which the + continuous hyper-cube environment (continuous version of a hyper-grid) in which the action space consists of the increment of each dimension d, modelled by a mixture - of Beta distributions. The states space is the value of each dimension. In order to + of beta distributions. the states space is the value of each dimension. in order to ensure that all trajectories are of finite length, actions have a minimum increment - for all dimensions determined by min_incr. If the value of any dimension is larger - than 1 - min_incr, then that dimension can be further incremented. In order to + for all dimensions determined by min_incr. if the value of any dimension is larger + than 1 - min_incr, then that dimension can be further incremented. in order to ensure the coverage of the state space, the first action (from the source state) is not constrained by the minimum increment. - Actions do not represent absolute increments but rather the relative increment with + actions do not represent absolute increments but rather the relative increment with respect to the distance to the edges of the hyper-cube, from the minimum increment. - That is, if dimension d of a state has value 0.3, the minimum increment (min_incr) + that is, if dimension d of a state has value 0.3, the minimum increment (min_incr) is 0.1 and the maximum value (max_val) is 1.0, an action of 0.5 will increment the - value of the dimension in 0.5 * (1.0 - 0.3 - 0.1) = 0.5 * 0.6 = 0.3. Therefore, the + value of the dimension in 0.5 * (1.0 - 0.3 - 0.1) = 0.5 * 0.6 = 0.3. therefore, the value of d in the next state will be 0.3 + 0.3 = 0.6. - Attributes + attributes ---------- n_dim : int - Dimensionality of the hyper-cube. + dimensionality of the hyper-cube. max_val : float - Max length of the hyper-cube. + max length of the hyper-cube. min_incr : float - Minimum increment in the actions, expressed as the fraction of max_val. This is + minimum increment in the actions, expressed as the fraction of max_val. this is necessary to ensure that trajectories have finite length. """ @@ -286,51 +286,51 @@ def __init__(self, **kwargs): def get_action_space(self): """ - The action space is continuous, thus not defined as such here. + the action space is continuous, thus not defined as such here. - The actions are tuples of length n_dim, where the value at position d indicates + the actions are tuples of length n_dim, where the value at position d indicates the increment of dimension d. - EOS is indicated by np.inf for all dimensions. + eos is indicated by np.inf for all dimensions. - This method defines self.eos and the returned action space is simply a + this method defines self.eos and the returned action space is simply a representative (arbitrary) action with an increment of 0.0 in all dimensions, - and EOS. + and eos. """ self.eos = tuple([np.inf] * self.n_dim) self.representative_action = tuple([0.0] * self.n_dim) return [self.representative_action, self.eos] - def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: + def get_policy_output(self, params: dict) -> tensortype["policy_output_dim"]: """ - Defines the structure of the output of the policy model, from which an + defines the structure of the output of the policy model, from which an action is to be determined or sampled, by returning a vector with a fixed - random policy. The environment consists of both continuous and discrete + random policy. the environment consists of both continuous and discrete actions. - Continuous actions + continuous actions - For each dimension d of the hyper-cube and component c of the mixture, the + for each dimension d of the hyper-cube and component c of the mixture, the output of the policy should return 1) the weight of the component in the mixture - 2) the logit(alpha) parameter of the Beta distribution to sample the increment - 3) the logit(beta) parameter of the Beta distribution to sample the increment + 2) the logit(alpha) parameter of the beta distribution to sample the increment + 3) the logit(beta) parameter of the beta distribution to sample the increment - These parameters are the first n_dim * n_comp * 3 of the policy output such - that the first 3 x C elements correspond to the first dimension, and so on. + these parameters are the first n_dim * n_comp * 3 of the policy output such + that the first 3 x c elements correspond to the first dimension, and so on. - Discrete actions + discrete actions - Additionally, the policy output contains one logit (pos -1) of a Bernoulli - distribution to model the (discrete) forward probability of selecting the EOS + additionally, the policy output contains one logit (pos -1) of a bernoulli + distribution to model the (discrete) forward probability of selecting the eos action and another logit (pos -2) for the (discrete) backward probability of returning to the source node. - Therefore, the output of the policy model has dimensionality D x C x 3 + 2, - where D is the number of dimensions (self.n_dim) and C is the number of + therefore, the output of the policy model has dimensionality d x c x 3 + 2, + where d is the number of dimensions (self.n_dim) and c is the number of components (self.n_comp). """ - # Parameters for continuous actions + # parameters for continuous actions self._len_policy_output_cont = self.n_dim * self.n_comp * 3 policy_output_cont = torch.empty( self._len_policy_output_cont, @@ -340,15 +340,15 @@ def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: policy_output_cont[0::3] = params["beta_weights"] policy_output_cont[1::3] = params["beta_alpha"] policy_output_cont[2::3] = params["beta_beta"] - # Logit for Bernoulli distribution to model EOS action + # logit for bernoulli distribution to model eos action policy_output_eos = torch.tensor( [params["bernoulli_eos_logit"]], dtype=self.float, device=self.device ) - # Logit for Bernoulli distribution to model back-to-source action + # logit for bernoulli distribution to model back-to-source action policy_output_source = torch.tensor( [params["bernoulli_source_logit"]], dtype=self.float, device=self.device ) - # Concatenate all outputs + # concatenate all outputs policy_output = torch.cat( ( policy_output_cont, @@ -359,91 +359,95 @@ def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: return policy_output def _get_policy_betas_weights( - self, policy_output: TensorType["n_states", "policy_output_dim"] - ) -> TensorType["n_states", "n_dim * n_comp"]: + self, policy_output: tensortype["n_states", "policy_output_dim"] + ) -> tensortype["n_states", "n_dim * n_comp"]: """ - Reduces a given policy output to the part corresponding to the weights of the - mixture of Beta distributions. + reduces a given policy output to the part corresponding to the weights of the + mixture of beta distributions. - See: get_policy_output() + see: get_policy_output() """ return policy_output[:, 0 : self._len_policy_output_cont : 3] def _get_policy_betas_alpha( - self, policy_output: TensorType["n_states", "policy_output_dim"] - ) -> TensorType["n_states", "n_dim * n_comp"]: + self, policy_output: tensortype["n_states", "policy_output_dim"] + ) -> tensortype["n_states", "n_dim * n_comp"]: """ - Reduces a given policy output to the part corresponding to the alphas of the - mixture of Beta distributions. + reduces a given policy output to the part corresponding to the alphas of the + mixture of beta distributions. - See: get_policy_output() + see: get_policy_output() """ return policy_output[:, 1 : self._len_policy_output_cont : 3] def _get_policy_betas_beta( - self, policy_output: TensorType["n_states", "policy_output_dim"] - ) -> TensorType["n_states", "n_dim * n_comp"]: + self, policy_output: tensortype["n_states", "policy_output_dim"] + ) -> tensortype["n_states", "n_dim * n_comp"]: """ - Reduces a given policy output to the part corresponding to the betas of the - mixture of Beta distributions. + reduces a given policy output to the part corresponding to the betas of the + mixture of beta distributions. - See: get_policy_output() + see: get_policy_output() """ return policy_output[:, 2 : self._len_policy_output_cont : 3] def _get_policy_eos_logit( - self, policy_output: TensorType["n_states", "policy_output_dim"] - ) -> TensorType["n_states", "1"]: + self, policy_output: tensortype["n_states", "policy_output_dim"] + ) -> tensortype["n_states", "1"]: """ - Reduces a given policy output to the part corresponding to the logit of the - Bernoulli distribution to model the EOS action. + reduces a given policy output to the part corresponding to the logit of the + bernoulli distribution to model the eos action. - See: get_policy_output() + see: get_policy_output() """ return policy_output[:, -1] def _get_policy_source_logit( - self, policy_output: TensorType["n_states", "policy_output_dim"] - ) -> TensorType["n_states", "1"]: + self, policy_output: tensortype["n_states", "policy_output_dim"] + ) -> tensortype["n_states", "1"]: """ - Reduces a given policy output to the part corresponding to the logit of the - Bernoulli distribution to model the back-to-source action. + reduces a given policy output to the part corresponding to the logit of the + bernoulli distribution to model the back-to-source action. - See: get_policy_output() + see: get_policy_output() """ return policy_output[:, -2] def get_mask_invalid_actions_forward( self, - state: Optional[List] = None, - done: Optional[bool] = None, - ) -> List: + state: optional[list] = none, + done: optional[bool] = none, + ) -> list: """ - The action space is continuous, thus the mask is not only of invalid actions as + the action space is continuous, thus the mask is not only of invalid actions as in discrete environments, but also an indicator of "special cases", for example states from which only certain actions are possible. - The values of True/False intend to approximately stick to the semantics in + the values of true/false intend to approximately stick to the semantics in discrete environments, where the mask is of "invalid" actions, but it is important to note that a direct interpretation in this sense does not always apply. - For example, the mask values of special cases are True if the special cases they - refer to are "invalid". In other words, the values are False if the state has + for example, the mask values of special cases are true if the special cases they + refer to are "invalid". in other words, the values are false if the state has the special case. - The forward mask has the following structure: + the forward mask has the following structure: - - 0 : whether a continuous action is invalid. True if the value at any - dimension is larger than 1 - min_incr, or if done is True. False otherwise. - - 1 : special case when the state is the source state. False when the state is - the source state, True otherwise. - - 2 : whether EOS action is invalid. EOS is valid from any state, except the - source state or if done is True. + - 0 : whether a continuous action is invalid. true if the value at any + dimension is larger than 1 - min_incr, or if done is true. false otherwise. + - 1 : special case when the state is the source state. false when the state is + the source state, true otherwise. + - 2 : whether eos action is invalid. eos is valid from any state, except the + source state or if done is true. + - -n_dim: : dimensions that should be ignored when sampling actions or + computing logprobs. this can be used for trajectories that may have + multiple dimensions coupled or fixed. for each dimension, true if ignored, + false, otherwise. By default, no dimension is ignored. """ state = self._get_state(state) done = self._get_done(done) - mask_dim = 3 + mask_dim = 3 + self.n_dim # If done, the entire mask is True (all actions are "invalid" and no special # cases) if done: @@ -481,15 +485,20 @@ def get_mask_invalid_actions_backward(self, state=None, done=None, parents_a=Non False if any dimension is smaller than min_incr, True otherwise. - 2 : whether EOS action is invalid. False only if done is True, True (invalid) otherwise. + - -n_dim: : dimensions that should be ignored when sampling actions or + computing logprobs. this can be used for trajectories that may have + multiple dimensions coupled or fixed. for each dimension, true if ignored, + false, otherwise. By default, no dimension is ignored. """ state = self._get_state(state) done = self._get_done(done) - mask_dim = 3 + mask_dim = 3 + self.n_dim mask = [True] * mask_dim # If done, only valid action is EOS. if done: mask[2] = False return mask + mask[-self.n_dim] = False # If any dimension is smaller than m, then back-to-source action is the only # possible actiona. if any([s < self.min_incr for s in state]): diff --git a/tests/gflownet/envs/test_ccube.py b/tests/gflownet/envs/test_ccube.py index 267854c04..ec53d62f9 100644 --- a/tests/gflownet/envs/test_ccube.py +++ b/tests/gflownet/envs/test_ccube.py @@ -95,19 +95,19 @@ def test__mask_backward__returns_all_true_except_eos_if_done(env, request): [ ( [0.0], - [False, False, True], + [False, False, True, False], ), ( [0.5], - [False, True, False], + [False, True, False, False], ), ( [0.90], - [False, True, False], + [False, True, False, False], ), ( [0.95], - [True, True, False], + [True, True, False, False], ), ], ) @@ -122,31 +122,31 @@ def test__mask_forward__1d__returns_expected(cube1d, state, mask_expected): [ ( [0.0, 0.0], - [False, False, True], + [False, False, True, False, False], ), ( [0.5, 0.5], - [False, True, False], + [False, True, False, False, False], ), ( [0.90, 0.5], - [False, True, False], + [False, True, False, False, False], ), ( [0.95, 0.5], - [True, True, False], + [True, True, False, False, False], ), ( [0.5, 0.90], - [False, True, False], + [False, True, False, False, False], ), ( [0.5, 0.95], - [True, True, False], + [True, True, False, False, False], ), ( [0.95, 0.95], - [True, True, False], + [True, True, False, False, False], ), ], ) @@ -161,27 +161,27 @@ def test__mask_forward__2d__returns_expected(cube2d, state, mask_expected): [ ( [0.0], - [True, False, True], + [True, False, True, False], ), ( [0.1], - [False, True, True], + [False, True, True, False], ), ( [0.05], - [True, False, True], + [True, False, True, False], ), ( [0.5], - [False, True, True], + [False, True, True, False], ), ( [0.90], - [False, True, True], + [False, True, True, False], ), ( [0.95], - [False, True, True], + [False, True, True, False], ), ], ) @@ -196,43 +196,43 @@ def test__mask_backward__1d__returns_expected(cube1d, state, mask_expected): [ ( [0.0, 0.0], - [True, False, True], + [True, False, True, False, False], ), ( [0.5, 0.5], - [False, True, True], + [False, True, True, False, False], ), ( [0.05, 0.5], - [True, False, True], + [True, False, True, False, False], ), ( [0.5, 0.05], - [True, False, True], + [True, False, True, False, False], ), ( [0.05, 0.05], - [True, False, True], + [True, False, True, False, False], ), ( [0.90, 0.5], - [False, True, True], + [False, True, True, False, False], ), ( [0.5, 0.90], - [False, True, True], + [False, True, True, False, False], ), ( [0.95, 0.5], - [False, True, True], + [False, True, True, False, False], ), ( [0.5, 0.95], - [False, True, True], + [False, True, True, False, False], ), ( [0.95, 0.95], - [False, True, True], + [False, True, True, False, False], ), ], ) From b529bb3be3a6255457e99ff71c062787d72383e0 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 16 Sep 2023 11:12:17 -0400 Subject: [PATCH 005/205] Add is_effective_dim argument to _make_increments_distribution (WIP) --- gflownet/envs/cube.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index b01e17359..12675b34c 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -586,6 +586,7 @@ def absolute_to_relative_increments( def _make_increments_distribution( self, policy_outputs: TensorType["n_states", "policy_output_dim"], + is_effective_dim: TensorType["n_states", "n_dim"], ) -> MixtureSameFamily: mix_logits = self._get_policy_betas_weights(policy_outputs).reshape( -1, self.n_dim, self.n_comp @@ -671,6 +672,8 @@ def _sample_actions_batch_forward( # Initialize variables n_states = policy_outputs.shape[0] is_eos = torch.zeros(n_states, dtype=torch.bool, device=self.device) + # Mask of effective dimensions + is_effective_dim = ~mask[-self.n_dim :] # Determine source states is_source = ~mask[:, 1] # EOS is the only possible action if continuous actions are invalid (mask[0] is @@ -694,7 +697,7 @@ def _sample_actions_batch_forward( raise NotImplementedError() elif sampling_method == "policy": distr_increments = self._make_increments_distribution( - policy_outputs[do_increments] + policy_outputs[do_increments], is_effective_dim ) # Shape of increments_rel: [n_do_increments, n_dim] increments_rel = distr_increments.sample() @@ -782,7 +785,7 @@ def _sample_actions_batch_backward( raise NotImplementedError() elif sampling_method == "policy": distr_increments = self._make_increments_distribution( - policy_outputs[do_increments] + policy_outputs[do_increments], is_effective_dim ) # Shape of increments_rel: [n_do_increments, n_dim] increments_rel = distr_increments.sample() @@ -922,7 +925,7 @@ def _get_logprobs_forward( ) # Get logprobs distr_increments = self._make_increments_distribution( - policy_outputs[do_increments] + policy_outputs[do_increments], is_effective_dim ) # Clamp because increments of 0.0 or 1.0 would yield nan logprobs_increments_rel[do_increments] = distr_increments.log_prob( @@ -1002,7 +1005,7 @@ def _get_logprobs_backward( ) # Get logprobs distr_increments = self._make_increments_distribution( - policy_outputs[do_increments] + policy_outputs[do_increments], is_effective_dim ) # Clamp because increments of 0.0 or 1.0 would yield nan logprobs_increments_rel[do_increments] = distr_increments.log_prob( From 68aebcd2e7028f0ac3b6b1c98b9c3169d930ca76 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 16 Sep 2023 12:33:30 -0400 Subject: [PATCH 006/205] Restore messed up capital letters in previous commit. --- gflownet/envs/cube.py | 292 +++++++++++++++++++++--------------------- 1 file changed, 146 insertions(+), 146 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 12675b34c..809ffd3c7 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -1,42 +1,42 @@ """ -classes to represent hyper-cube environments +Classes to represent hyper-cube environments """ import itertools -from abc import abc, abstractmethod -from typing import list, optional, tuple +from abc import ABC, abstractmethod +from typing import List, Optional, Tuple import matplotlib.pyplot as plt import numpy as np import numpy.typing as npt import pandas as pd import torch -from sklearn.neighbors import kerneldensity -from torch.distributions import bernoulli, beta, categorical, mixturesamefamily, uniform -from torchtyping import tensortype +from sklearn.neighbors import KernelDensity +from torch.distributions import Bernoulli, Beta, Categorical, MixtureSameFamily, Uniform +from torchtyping import TensorType -from gflownet.envs.base import gflownetenv +from gflownet.envs.base import GFlowNetEnv from gflownet.utils.common import copy, tbool, tfloat -class cube(gflownetenv, abc): +class Cube(GFlowNetEnv, ABC): """ - continuous (hybrid: discrete and continuous) hyper-cube environment (continuous + Continuous (hybrid: discrete and continuous) hyper-cube environment (continuous version of a hyper-grid) in which the action space consists of the increment of dimension d, modelled by a beta distribution. - the states space is the value of each dimension. if the value of a dimension gets + The states space is the value of each dimension. If the value of a dimension gets larger than max_val, then the trajectory is ended. - attributes + Attributes ---------- n_dim : int - dimensionality of the hyper-cube. + Dimensionality of the hyper-cube. max_val : float - max length of the hyper-cube. + Max length of the hyper-cube. min_incr : float - minimum increment in the actions, expressed as the fraction of max_val. this is + Minimum increment in the actions, expressed as the fraction of max_val. This is necessary to ensure coverage of the state space. """ @@ -67,22 +67,22 @@ def __init__( assert n_dim > 0 assert max_val > 0.0 assert n_comp > 0 - # main properties + # Main properties self.n_dim = n_dim self.eos = self.n_dim self.max_val = max_val self.min_incr = min_incr * self.max_val - # parameters of the policy distribution + # Parameters of the policy distribution self.n_comp = n_comp self.beta_params_min = beta_params_min self.beta_params_max = beta_params_max - # source state: position 0 at all dimensions + # Source state: position 0 at all dimensions self.source = [0.0 for _ in range(self.n_dim)] - # action from source: (n_dim, 0) + # Action from source: (n_dim, 0) self.action_source = (self.n_dim, 0) - # end-of-sequence action: (n_dim + 1, 0) + # End-of-sequence action: (n_dim + 1, 0) self.eos = (self.n_dim + 1, 0) - # conversions: only conversions to policy are implemented and the rest are the + # Conversions: only conversions to policy are implemented and the rest are the # same self.state2proxy = self.state2policy self.statebatch2proxy = self.statebatch2policy @@ -90,194 +90,194 @@ def __init__( self.state2oracle = self.state2proxy self.statebatch2oracle = self.statebatch2proxy self.statetorch2oracle = self.statetorch2proxy - # base class init + # Base class init super().__init__( fixed_distr_params=fixed_distr_params, random_distr_params=random_distr_params, **kwargs, ) - self.continuous = true + self.continuous = True @abstractmethod def get_action_space(self): pass @abstractmethod - def get_policy_output(self, params: dict) -> tensortype["policy_output_dim"]: + def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: pass @abstractmethod def get_mask_invalid_actions_forward( self, - state: optional[list] = none, - done: optional[bool] = none, - ) -> list: + state: Optional[List] = None, + done: Optional[bool] = None, + ) -> List: pass @abstractmethod - def get_mask_invalid_actions_backward(self, state=none, done=none, parents_a=none): + def get_mask_invalid_actions_backward(self, state=None, done=None, parents_a=None): pass def statetorch2policy( - self, states: tensortype["batch", "state_dim"] = none - ) -> tensortype["batch", "policy_input_dim"]: + self, states: TensorType["batch", "state_dim"] = None + ) -> TensorType["batch", "policy_input_dim"]: """ - clips the states into [0, max_val] and maps them to [-1.0, 1.0] + Clips the states into [0, max_val] and maps them to [-1.0, 1.0] - args + Args ---- state : list - state + State """ return 2.0 * torch.clip(states, min=0.0, max=self.max_val) - 1.0 def statebatch2policy( - self, states: list[list] - ) -> tensortype["batch", "state_proxy_dim"]: + self, states: List[List] + ) -> TensorType["batch", "state_proxy_dim"]: """ - clips the states into [0, max_val] and maps them to [-1.0, 1.0] + Clips the states into [0, max_val] and maps them to [-1.0, 1.0] - args + Args ---- state : list - state + State """ return self.statetorch2policy( tfloat(states, device=self.device, float_type=self.float) ) - def state2policy(self, state: list = none) -> list: + def state2policy(self, state: List = None) -> List: """ - clips the state into [0, max_val] and maps it to [-1.0, 1.0] + Clips the state into [0, max_val] and maps it to [-1.0, 1.0] """ - if state is none: + if state is None: state = self.state.copy() return [2.0 * min(max(0.0, s), self.max_val) - 1.0 for s in state] - def state2readable(self, state: list) -> str: + def state2readable(self, state: List) -> str: """ - converts a state (a list of positions) into a human-readable string + Converts a state (a list of positions) into a human-readable string representing a state. """ return str(state).replace("(", "[").replace(")", "]").replace(",", "") - def readable2state(self, readable: str) -> list: + def readable2state(self, readable: str) -> List: """ - converts a human-readable string representing a state into a state as a list of + Converts a human-readable string representing a state into a state as a list of positions. """ return [el for el in readable.strip("[]").split(" ")] @abstractmethod def get_parents( - self, state: list = none, done: bool = none, action: tuple[int, float] = none - ) -> tuple[list[list], list[tuple[int, float]]]: + self, state: List = None, done: bool = None, action: Tuple[int, float] = None + ) -> Tuple[List[List], List[Tuple[int, float]]]: """ - determines all parents and actions that lead to state. + Determines all parents and actions that lead to state. - args + Args ---- state : list - representation of a state + Representation of a state done : bool - whether the trajectory is done. if none, done is taken from instance. + Whether the trajectory is done. If None, done is taken from instance. action : int - last action performed + Last action performed - returns + Returns ------- parents : list - list of parents in state format + List of parents in state format actions : list - list of actions that lead to state for each parent in parents + List of actions that lead to state for each parent in parents """ pass @abstractmethod def sample_actions_batch( self, - policy_outputs: tensortype["n_states", "policy_output_dim"], + policy_outputs: TensorType["n_states", "policy_output_dim"], sampling_method: str = "policy", - mask_invalid_actions: tensortype["n_states", "1"] = none, + mask_invalid_actions: TensorType["n_states", "1"] = None, temperature_logits: float = 1.0, loginf: float = 1000, - ) -> tuple[list[tuple], tensortype["n_states"]]: + ) -> Tuple[List[Tuple], TensorType["n_states"]]: """ - samples a batch of actions from a batch of policy outputs. + Samples a batch of actions from a batch of policy outputs. """ pass def get_logprobs( self, - policy_outputs: tensortype["n_states", "policy_output_dim"], + policy_outputs: TensorType["n_states", "policy_output_dim"], is_forward: bool, - actions: tensortype["n_states", 2], - mask_invalid_actions: tensortype["batch_size", "policy_output_dim"] = none, + actions: TensorType["n_states", 2], + mask_invalid_actions: TensorType["batch_size", "policy_output_dim"] = None, loginf: float = 1000, - ) -> tensortype["batch_size"]: + ) -> TensorType["batch_size"]: """ - computes log probabilities of actions given policy outputs and actions. + Computes log probabilities of actions given policy outputs and actions. """ pass def step( - self, action: tuple[int, float] - ) -> tuple[list[float], tuple[int, float], bool]: + self, action: Tuple[int, float] + ) -> Tuple[List[float], Tuple[int, float], bool]: """ - executes step given an action. + Executes step given an action. - args + Args ---- action : tuple - action to be executed. an action is a tuple with two values: + Action to be executed. An action is a tuple with two values: (dimension, increment). - returns + Returns ------- self.state : list - the sequence after executing the action + The sequence after executing the action action : int - action executed + Action executed valid : bool - false, if the action is not allowed for the current state, e.g. stop at the + False, if the action is not allowed for the current state, e.g. stop at the root state """ pass -class continuouscube(cube): +class ContinuousCube(Cube): """ - continuous hyper-cube environment (continuous version of a hyper-grid) in which the + Continuous hyper-cube environment (continuous version of a hyper-grid) in which the action space consists of the increment of each dimension d, modelled by a mixture - of beta distributions. the states space is the value of each dimension. in order to + of Beta distributions. The states space is the value of each dimension. In order to ensure that all trajectories are of finite length, actions have a minimum increment - for all dimensions determined by min_incr. if the value of any dimension is larger - than 1 - min_incr, then that dimension can be further incremented. in order to + for all dimensions determined by min_incr. If the value of any dimension is larger + than 1 - min_incr, then that dimension can be further incremented. In order to ensure the coverage of the state space, the first action (from the source state) is not constrained by the minimum increment. - actions do not represent absolute increments but rather the relative increment with + Actions do not represent absolute increments but rather the relative increment with respect to the distance to the edges of the hyper-cube, from the minimum increment. - that is, if dimension d of a state has value 0.3, the minimum increment (min_incr) + That is, if dimension d of a state has value 0.3, the minimum increment (min_incr) is 0.1 and the maximum value (max_val) is 1.0, an action of 0.5 will increment the - value of the dimension in 0.5 * (1.0 - 0.3 - 0.1) = 0.5 * 0.6 = 0.3. therefore, the + value of the dimension in 0.5 * (1.0 - 0.3 - 0.1) = 0.5 * 0.6 = 0.3. Therefore, the value of d in the next state will be 0.3 + 0.3 = 0.6. - attributes + Attributes ---------- n_dim : int - dimensionality of the hyper-cube. + Dimensionality of the hyper-cube. max_val : float - max length of the hyper-cube. + Max length of the hyper-cube. min_incr : float - minimum increment in the actions, expressed as the fraction of max_val. this is + Minimum increment in the actions, expressed as the fraction of max_val. This is necessary to ensure that trajectories have finite length. """ @@ -286,51 +286,51 @@ def __init__(self, **kwargs): def get_action_space(self): """ - the action space is continuous, thus not defined as such here. + The action space is continuous, thus not defined as such here. - the actions are tuples of length n_dim, where the value at position d indicates + The actions are tuples of length n_dim, where the value at position d indicates the increment of dimension d. - eos is indicated by np.inf for all dimensions. + EOS is indicated by np.inf for all dimensions. - this method defines self.eos and the returned action space is simply a + This method defines self.eos and the returned action space is simply a representative (arbitrary) action with an increment of 0.0 in all dimensions, - and eos. + and EOS. """ self.eos = tuple([np.inf] * self.n_dim) self.representative_action = tuple([0.0] * self.n_dim) return [self.representative_action, self.eos] - def get_policy_output(self, params: dict) -> tensortype["policy_output_dim"]: + def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: """ - defines the structure of the output of the policy model, from which an + Defines the structure of the output of the policy model, from which an action is to be determined or sampled, by returning a vector with a fixed - random policy. the environment consists of both continuous and discrete + random policy. The environment consists of both continuous and discrete actions. - continuous actions + Continuous actions - for each dimension d of the hyper-cube and component c of the mixture, the + For each dimension d of the hyper-cube and component c of the mixture, the output of the policy should return 1) the weight of the component in the mixture - 2) the logit(alpha) parameter of the beta distribution to sample the increment - 3) the logit(beta) parameter of the beta distribution to sample the increment + 2) the logit(alpha) parameter of the Beta distribution to sample the increment + 3) the logit(beta) parameter of the Beta distribution to sample the increment - these parameters are the first n_dim * n_comp * 3 of the policy output such - that the first 3 x c elements correspond to the first dimension, and so on. + These parameters are the first n_dim * n_comp * 3 of the policy output such + that the first 3 x C elements correspond to the first dimension, and so on. - discrete actions + Discrete actions - additionally, the policy output contains one logit (pos -1) of a bernoulli - distribution to model the (discrete) forward probability of selecting the eos + Additionally, the policy output contains one logit (pos -1) of a Bernoulli + distribution to model the (discrete) forward probability of selecting the EOS action and another logit (pos -2) for the (discrete) backward probability of returning to the source node. - therefore, the output of the policy model has dimensionality d x c x 3 + 2, - where d is the number of dimensions (self.n_dim) and c is the number of + Therefore, the output of the policy model has dimensionality D x C x 3 + 2, + where D is the number of dimensions (self.n_dim) and C is the number of components (self.n_comp). """ - # parameters for continuous actions + # Parameters for continuous actions self._len_policy_output_cont = self.n_dim * self.n_comp * 3 policy_output_cont = torch.empty( self._len_policy_output_cont, @@ -340,15 +340,15 @@ def get_policy_output(self, params: dict) -> tensortype["policy_output_dim"]: policy_output_cont[0::3] = params["beta_weights"] policy_output_cont[1::3] = params["beta_alpha"] policy_output_cont[2::3] = params["beta_beta"] - # logit for bernoulli distribution to model eos action + # Logit for Bernoulli distribution to model EOS action policy_output_eos = torch.tensor( [params["bernoulli_eos_logit"]], dtype=self.float, device=self.device ) - # logit for bernoulli distribution to model back-to-source action + # Logit for Bernoulli distribution to model back-to-source action policy_output_source = torch.tensor( [params["bernoulli_source_logit"]], dtype=self.float, device=self.device ) - # concatenate all outputs + # Concatenate all outputs policy_output = torch.cat( ( policy_output_cont, @@ -359,87 +359,87 @@ def get_policy_output(self, params: dict) -> tensortype["policy_output_dim"]: return policy_output def _get_policy_betas_weights( - self, policy_output: tensortype["n_states", "policy_output_dim"] - ) -> tensortype["n_states", "n_dim * n_comp"]: + self, policy_output: TensorType["n_states", "policy_output_dim"] + ) -> TensorType["n_states", "n_dim * n_comp"]: """ - reduces a given policy output to the part corresponding to the weights of the - mixture of beta distributions. + Reduces a given policy output to the part corresponding to the weights of the + mixture of Beta distributions. - see: get_policy_output() + See: get_policy_output() """ return policy_output[:, 0 : self._len_policy_output_cont : 3] def _get_policy_betas_alpha( - self, policy_output: tensortype["n_states", "policy_output_dim"] - ) -> tensortype["n_states", "n_dim * n_comp"]: + self, policy_output: TensorType["n_states", "policy_output_dim"] + ) -> TensorType["n_states", "n_dim * n_comp"]: """ - reduces a given policy output to the part corresponding to the alphas of the - mixture of beta distributions. + Reduces a given policy output to the part corresponding to the alphas of the + mixture of Beta distributions. - see: get_policy_output() + See: get_policy_output() """ return policy_output[:, 1 : self._len_policy_output_cont : 3] def _get_policy_betas_beta( - self, policy_output: tensortype["n_states", "policy_output_dim"] - ) -> tensortype["n_states", "n_dim * n_comp"]: + self, policy_output: TensorType["n_states", "policy_output_dim"] + ) -> TensorType["n_states", "n_dim * n_comp"]: """ - reduces a given policy output to the part corresponding to the betas of the - mixture of beta distributions. + Reduces a given policy output to the part corresponding to the betas of the + mixture of Beta distributions. - see: get_policy_output() + See: get_policy_output() """ return policy_output[:, 2 : self._len_policy_output_cont : 3] def _get_policy_eos_logit( - self, policy_output: tensortype["n_states", "policy_output_dim"] - ) -> tensortype["n_states", "1"]: + self, policy_output: TensorType["n_states", "policy_output_dim"] + ) -> TensorType["n_states", "1"]: """ - reduces a given policy output to the part corresponding to the logit of the - bernoulli distribution to model the eos action. + Reduces a given policy output to the part corresponding to the logit of the + Bernoulli distribution to model the EOS action. - see: get_policy_output() + See: get_policy_output() """ return policy_output[:, -1] def _get_policy_source_logit( - self, policy_output: tensortype["n_states", "policy_output_dim"] - ) -> tensortype["n_states", "1"]: + self, policy_output: TensorType["n_states", "policy_output_dim"] + ) -> TensorType["n_states", "1"]: """ - reduces a given policy output to the part corresponding to the logit of the - bernoulli distribution to model the back-to-source action. + Reduces a given policy output to the part corresponding to the logit of the + Bernoulli distribution to model the back-to-source action. - see: get_policy_output() + See: get_policy_output() """ return policy_output[:, -2] def get_mask_invalid_actions_forward( self, - state: optional[list] = none, - done: optional[bool] = none, - ) -> list: + state: Optional[List] = None, + done: Optional[bool] = None, + ) -> List: """ - the action space is continuous, thus the mask is not only of invalid actions as + The action space is continuous, thus the mask is not only of invalid actions as in discrete environments, but also an indicator of "special cases", for example states from which only certain actions are possible. - the values of true/false intend to approximately stick to the semantics in + The values of True/False intend to approximately stick to the semantics in discrete environments, where the mask is of "invalid" actions, but it is important to note that a direct interpretation in this sense does not always apply. - for example, the mask values of special cases are true if the special cases they - refer to are "invalid". in other words, the values are false if the state has + For example, the mask values of special cases are True if the special cases they + refer to are "invalid". In other words, the values are False if the state has the special case. - the forward mask has the following structure: + The forward mask has the following structure: - - 0 : whether a continuous action is invalid. true if the value at any - dimension is larger than 1 - min_incr, or if done is true. false otherwise. - - 1 : special case when the state is the source state. false when the state is - the source state, true otherwise. - - 2 : whether eos action is invalid. eos is valid from any state, except the - source state or if done is true. + - 0 : whether a continuous action is invalid. True if the value at any + dimension is larger than 1 - min_incr, or if done is True. False otherwise. + - 1 : special case when the state is the source state. False when the state is + the source state, True otherwise. + - 2 : whether EOS action is invalid. EOS is valid from any state, except the + source state or if done is True. - -n_dim: : dimensions that should be ignored when sampling actions or computing logprobs. this can be used for trajectories that may have multiple dimensions coupled or fixed. for each dimension, true if ignored, From ccaaa6b16ec7ceeae6d876f46985d23747747b5f Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 16 Sep 2023 12:38:06 -0400 Subject: [PATCH 007/205] Small fix in backward mask. --- gflownet/envs/cube.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 809ffd3c7..030e7ac46 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -498,7 +498,7 @@ def get_mask_invalid_actions_backward(self, state=None, done=None, parents_a=Non if done: mask[2] = False return mask - mask[-self.n_dim] = False + mask[-self.n_dim :] = False # If any dimension is smaller than m, then back-to-source action is the only # possible actiona. if any([s < self.min_incr for s in state]): From afea1bcd4471229a2032e381c2f4934ba6401aef Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 16 Sep 2023 14:38:49 -0400 Subject: [PATCH 008/205] Fixes to ensure tests are passed --- gflownet/envs/cube.py | 8 +++++++- tests/gflownet/envs/test_ccube.py | 4 ++-- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 030e7ac46..c4ac1a1d5 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -498,7 +498,7 @@ def get_mask_invalid_actions_backward(self, state=None, done=None, parents_a=Non if done: mask[2] = False return mask - mask[-self.n_dim :] = False + mask = [True] * 3 + [False] * self.n_dim # If any dimension is smaller than m, then back-to-source action is the only # possible actiona. if any([s < self.min_incr for s in state]): @@ -765,6 +765,8 @@ def _sample_actions_batch_backward( # Initialize variables n_states = policy_outputs.shape[0] is_bts = torch.zeros(n_states, dtype=torch.bool, device=self.device) + # Mask of effective dimensions + is_effective_dim = ~mask[-self.n_dim :] # EOS is the only possible action only if done is True (mask[2] is False) is_eos = ~mask[:, 2] # Back-to-source (BTS) is the only possible action if mask[1] is False @@ -885,6 +887,8 @@ def _get_logprobs_forward( (n_states, self.n_dim), device=self.device, dtype=self.float ) eos_tensor = tfloat(self.eos, float_type=self.float, device=self.device) + # Mask of effective dimensions + is_effective_dim = ~mask[-self.n_dim :] # Determine source states is_source = ~mask[:, 1] # EOS is the only possible action if continuous actions are invalid (mask[0] is @@ -968,6 +972,8 @@ def _get_logprobs_backward( jacobian_diag = torch.ones( (n_states, self.n_dim), device=self.device, dtype=self.float ) + # Mask of effective dimensions + is_effective_dim = ~mask[-self.n_dim :] # EOS is the only possible action only if done is True (mask[2] is False) is_eos = ~mask[:, 2] # Back-to-source (BTS) is the only possible action if mask[1] is False diff --git a/tests/gflownet/envs/test_ccube.py b/tests/gflownet/envs/test_ccube.py index ec53d62f9..64df299ff 100644 --- a/tests/gflownet/envs/test_ccube.py +++ b/tests/gflownet/envs/test_ccube.py @@ -86,8 +86,8 @@ def test__mask_backward__returns_all_true_except_eos_if_done(env, request): for state in states: env.set_state(state, done=True) mask = env.get_mask_invalid_actions_backward() - assert all(mask[:-1]) - assert mask[-1] is False + assert all(mask[:2]) + assert mask[2] is False @pytest.mark.parametrize( From fa946965fbe7e33136358026a67238c5bdabbd71 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 17 Sep 2023 15:03:16 -0400 Subject: [PATCH 009/205] Mask - make zero - the entries of the ignored dimensions of the actions, logprobs of increments and log of the diagonal of the Jacobian. --- gflownet/envs/cube.py | 101 ++++++++++++++++++++++++++++++------------ 1 file changed, 73 insertions(+), 28 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index c4ac1a1d5..ed47b5ff2 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -586,7 +586,6 @@ def absolute_to_relative_increments( def _make_increments_distribution( self, policy_outputs: TensorType["n_states", "policy_output_dim"], - is_effective_dim: TensorType["n_states", "n_dim"], ) -> MixtureSameFamily: mix_logits = self._get_policy_betas_weights(policy_outputs).reshape( -1, self.n_dim, self.n_comp @@ -603,6 +602,34 @@ def _make_increments_distribution( beta_distr = Beta(alphas, betas) return MixtureSameFamily(mix, beta_distr) + def _mask_ignored_dimensions( + self, + mask: TensorType["n_states", "policy_outputs_dim"], + tensor_to_mask: TensorType["n_states", "n_dim"], + ) -> MixtureSameFamily: + """ + Makes the actions, logprobs or log jacobian entries of ignored dimensions zero. + + Since the shape of all the tensor of actions, the logprobs of increments and + the log of the diagonal of the Jacobian must be the same, this method makes no + distiction between for simplicity. + + Args + ---- + mask : tensor + Boolean mask indicating (True) which dimensions should be set to zero. + + tensor_to_mask : tensor + Tensor to be modified. It may be a tensor of actions, of logprobs of + increments or the log of the diagonal of the Jacobian. + """ + is_ignored_dim = mask[:, -self.n_dim :] + if torch.any(is_ignored_dim): + shape_orig = tensor_to_mask.shape + tensor_to_mask[is_ignored_dim] = 0.0 + tensor_to_mask = tensor_to_mask.reshape(shape_orig) + return tensor_to_mask + def sample_actions_batch( self, policy_outputs: TensorType["n_states", "policy_output_dim"], @@ -672,8 +699,6 @@ def _sample_actions_batch_forward( # Initialize variables n_states = policy_outputs.shape[0] is_eos = torch.zeros(n_states, dtype=torch.bool, device=self.device) - # Mask of effective dimensions - is_effective_dim = ~mask[-self.n_dim :] # Determine source states is_source = ~mask[:, 1] # EOS is the only possible action if continuous actions are invalid (mask[0] is @@ -697,7 +722,7 @@ def _sample_actions_batch_forward( raise NotImplementedError() elif sampling_method == "policy": distr_increments = self._make_increments_distribution( - policy_outputs[do_increments], is_effective_dim + policy_outputs[do_increments] ) # Shape of increments_rel: [n_do_increments, n_dim] increments_rel = distr_increments.sample() @@ -723,6 +748,8 @@ def _sample_actions_batch_forward( ) if torch.any(do_increments): actions_tensor[do_increments] = increments_abs + # Make ignored dimensions zero + actions_tensor = self._mask_ignored_dimensions(mask, actions_tensor) actions = [tuple(a.tolist()) for a in actions_tensor] return actions, None @@ -766,7 +793,7 @@ def _sample_actions_batch_backward( n_states = policy_outputs.shape[0] is_bts = torch.zeros(n_states, dtype=torch.bool, device=self.device) # Mask of effective dimensions - is_effective_dim = ~mask[-self.n_dim :] + is_effective_dim = ~mask[:, -self.n_dim :] # EOS is the only possible action only if done is True (mask[2] is False) is_eos = ~mask[:, 2] # Back-to-source (BTS) is the only possible action if mask[1] is False @@ -787,7 +814,7 @@ def _sample_actions_batch_backward( raise NotImplementedError() elif sampling_method == "policy": distr_increments = self._make_increments_distribution( - policy_outputs[do_increments], is_effective_dim + policy_outputs[do_increments] ) # Shape of increments_rel: [n_do_increments, n_dim] increments_rel = distr_increments.sample() @@ -813,6 +840,8 @@ def _sample_actions_batch_backward( actions_tensor[is_eos] = torch.inf if torch.any(do_increments): actions_tensor[do_increments] = increments_abs + # Make ignored dimensions zero + actions_tensor = self._mask_ignored_dimensions(mask, actions_tensor) if torch.any(is_bts): # BTS actions are equal to the originating states actions_bts = tfloat( @@ -883,12 +912,12 @@ def _get_logprobs_forward( logprobs_increments_rel = torch.zeros( (n_states, self.n_dim), dtype=self.float, device=self.device ) - jacobian_diag = torch.ones( + log_jacobian_diag = torch.zeros( (n_states, self.n_dim), device=self.device, dtype=self.float ) eos_tensor = tfloat(self.eos, float_type=self.float, device=self.device) # Mask of effective dimensions - is_effective_dim = ~mask[-self.n_dim :] + is_effective_dim = ~mask[:, -self.n_dim :] # Determine source states is_source = ~mask[:, 1] # EOS is the only possible action if continuous actions are invalid (mask[0] is @@ -929,21 +958,29 @@ def _get_logprobs_forward( ) # Get logprobs distr_increments = self._make_increments_distribution( - policy_outputs[do_increments], is_effective_dim + policy_outputs[do_increments] ) # Clamp because increments of 0.0 or 1.0 would yield nan logprobs_increments_rel[do_increments] = distr_increments.log_prob( torch.clamp(increments_rel, min=1e-6, max=(1 - 1e-6)) ) - # Compute diagonal of the Jacobian (see _get_jacobian_diag()) - jacobian_diag[do_increments] = self._get_jacobian_diag( - states_from_tensor[do_increments], - min_increments, - self.max_val, - is_backward=False, + # Make ignored dimensions zero + logprobs_increments_rel = self._mask_ignored_dimensions( + mask, logprobs_increments_rel + ) + # Compute log of the diagonal of the Jacobian (see _get_jacobian_diag()) + log_jacobian_diag[do_increments] = torch.log( + self._get_jacobian_diag( + states_from_tensor[do_increments], + min_increments, + self.max_val, + is_backward=False, + ) ) - # Get log determinant of the Jacobian - log_det_jacobian = torch.sum(torch.log(jacobian_diag), dim=1) + # Make ignored dimensions zero + log_jacobian_diag = self._mask_ignored_dimensions(mask, log_jacobian_diag) + # Sum log Jacobian across dimensions + log_det_jacobian = torch.sum(log_jacobian_diag, dim=1) # Compute combined probabilities sumlogprobs_increments = logprobs_increments_rel.sum(axis=1) logprobs = logprobs_eos + sumlogprobs_increments + log_det_jacobian @@ -969,11 +1006,11 @@ def _get_logprobs_backward( logprobs_increments_rel = torch.zeros( (n_states, self.n_dim), dtype=self.float, device=self.device ) - jacobian_diag = torch.ones( + log_jacobian_diag = torch.zeros( (n_states, self.n_dim), device=self.device, dtype=self.float ) # Mask of effective dimensions - is_effective_dim = ~mask[-self.n_dim :] + is_effective_dim = ~mask[:, -self.n_dim :] # EOS is the only possible action only if done is True (mask[2] is False) is_eos = ~mask[:, 2] # Back-to-source (BTS) is the only possible action if mask[1] is False @@ -1011,21 +1048,29 @@ def _get_logprobs_backward( ) # Get logprobs distr_increments = self._make_increments_distribution( - policy_outputs[do_increments], is_effective_dim + policy_outputs[do_increments] ) # Clamp because increments of 0.0 or 1.0 would yield nan logprobs_increments_rel[do_increments] = distr_increments.log_prob( torch.clamp(increments_rel, min=1e-6, max=(1 - 1e-6)) ) - # Compute diagonal of the Jacobian (see _get_jacobian_diag()) - jacobian_diag[do_increments] = self._get_jacobian_diag( - states_from_tensor[do_increments], - min_increments, - self.max_val, - is_backward=True, + # Make ignored dimensions zero + logprobs_increments_rel = self._mask_ignored_dimensions( + mask, logprobs_increments_rel + ) + # Compute log of the diagonal of the Jacobian (see _get_jacobian_diag()) + log_jacobian_diag[do_increments] = torch.log( + self._get_jacobian_diag( + states_from_tensor[do_increments], + min_increments, + self.max_val, + is_backward=True, + ) ) - # Get log determinant of the Jacobian - log_det_jacobian = torch.sum(torch.log(jacobian_diag), dim=1) + # Make ignored dimensions zero + log_jacobian_diag = self._mask_ignored_dimensions(mask, log_jacobian_diag) + # Sum log Jacobian across dimensions + log_det_jacobian = torch.sum(log_jacobian_diag, dim=1) # Compute combined probabilities sumlogprobs_increments = logprobs_increments_rel.sum(axis=1) logprobs = logprobs_bts + sumlogprobs_increments + log_det_jacobian From b998f902dee6b2e59b33958e82b8fb6eb70ea261 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 18 Sep 2023 16:46:14 -0400 Subject: [PATCH 010/205] Add attribute ignored_dim to Cube environment and handle the masks and the comparisons with the source by taking into account the effective dimensions only. --- gflownet/envs/cube.py | 62 +++++++++++++++++++++++++++++++------------ 1 file changed, 45 insertions(+), 17 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index ed47b5ff2..89d070e80 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -38,6 +38,11 @@ class Cube(GFlowNetEnv, ABC): min_incr : float Minimum increment in the actions, expressed as the fraction of max_val. This is necessary to ensure coverage of the state space. + + ignored_dims : list + Boolean mask of ignored dimensions. This can be used for trajectories that may + have multiple dimensions coupled or fixed. For each dimension, True if ignored, + False, otherwise. If None, no dimension is ignored. """ def __init__( @@ -46,6 +51,7 @@ def __init__( max_val: float = 1.0, min_incr: float = 0.1, n_comp: int = 1, + ignored_dims: Optional[List[bool]] = None, beta_params_min: float = 0.1, beta_params_max: float = 1000.0, fixed_distr_params: dict = { @@ -72,6 +78,10 @@ def __init__( self.eos = self.n_dim self.max_val = max_val self.min_incr = min_incr * self.max_val + if ignored_dims: + self.ignored_dims = ignored_dims + else: + self.ignored_dims = [False] * self.n_dim # Parameters of the policy distribution self.n_comp = n_comp self.beta_params_min = beta_params_min @@ -249,6 +259,10 @@ def step( """ pass + def _get_effective_dims(self, state: Optional[List] = None) -> List: + state = self._get_state(state) + return [s for s, ign_dim in zip(state, self.ignored_dims) if not ign_dim] + class ContinuousCube(Cube): """ @@ -441,27 +455,28 @@ def get_mask_invalid_actions_forward( - 2 : whether EOS action is invalid. EOS is valid from any state, except the source state or if done is True. - -n_dim: : dimensions that should be ignored when sampling actions or - computing logprobs. this can be used for trajectories that may have - multiple dimensions coupled or fixed. for each dimension, true if ignored, - false, otherwise. By default, no dimension is ignored. + computing logprobs. This can be used for trajectories that may have + multiple dimensions coupled or fixed. For each dimension, True if ignored, + False, otherwise. """ state = self._get_state(state) done = self._get_done(done) - mask_dim = 3 + self.n_dim + mask_dim_base = 3 + mask_dim = mask_dim_base + self.n_dim # If done, the entire mask is True (all actions are "invalid" and no special # cases) if done: return [True] * mask_dim - mask = [False] * mask_dim - # If the state is not the source state, EOS is invalid - if state == self.source: + mask = [False] * mask_dim_base + self.ignored_dims + # If the state is the source state, EOS is invalid + if self._get_effective_dims(state) == self._get_effective_dims(self.source): mask[2] = True # If the state is not the source, indicate not special case (True) else: mask[1] = True # If the value of any dimension is greater than 1 - min_incr, then continuous # actions are invalid (True). - if any([s > 1 - self.min_incr for s in state]): + if any([s > 1 - self.min_incr for s in self._get_effective_dims(state)]): mask[0] = True return mask @@ -492,16 +507,15 @@ def get_mask_invalid_actions_backward(self, state=None, done=None, parents_a=Non """ state = self._get_state(state) done = self._get_done(done) - mask_dim = 3 + self.n_dim - mask = [True] * mask_dim + mask_dim_base = 3 + mask = [True] * mask_dim_base + self.ignored_dims # If done, only valid action is EOS. if done: mask[2] = False return mask - mask = [True] * 3 + [False] * self.n_dim # If any dimension is smaller than m, then back-to-source action is the only # possible actiona. - if any([s < self.min_incr for s in state]): + if any([s < self.min_incr for s in self._get_effective_dims(state)]): mask[1] = False return mask # Otherwise, continuous actions are valid @@ -749,7 +763,9 @@ def _sample_actions_batch_forward( if torch.any(do_increments): actions_tensor[do_increments] = increments_abs # Make ignored dimensions zero - actions_tensor = self._mask_ignored_dimensions(mask, actions_tensor) + actions_tensor[do_increments] = self._mask_ignored_dimensions( + mask[do_increments], actions_tensor[do_increments] + ) actions = [tuple(a.tolist()) for a in actions_tensor] return actions, None @@ -841,13 +857,19 @@ def _sample_actions_batch_backward( if torch.any(do_increments): actions_tensor[do_increments] = increments_abs # Make ignored dimensions zero - actions_tensor = self._mask_ignored_dimensions(mask, actions_tensor) + actions_tensor[do_increments] = self._mask_ignored_dimensions( + mask[do_increments], actions_tensor[do_increments] + ) if torch.any(is_bts): # BTS actions are equal to the originating states actions_bts = tfloat( states_from, float_type=self.float, device=self.device )[is_bts] actions_tensor[is_bts] = actions_bts + # Make ignored dimensions zero + actions_tensor[is_bts] = self._mask_ignored_dimensions( + mask[is_bts], actions_tensor[is_bts] + ) actions = [tuple(a.tolist()) for a in actions_tensor] return actions, None @@ -1159,8 +1181,12 @@ def _step( else: self.state[dim] += incr # If state is close enough to source, set source to avoid escaping comparison - # to source. - if self.isclose(self.state, self.source, atol=1e-6): + # to source. Only effective dimensions (not ignored) are considered. + if self.isclose( + self._get_effective_dims(self.state), + self._get_effective_dims(self.source), + atol=1e-6, + ): self.state = copy(self.source) if not all([s <= (self.max_val + epsilon) for s in self.state]): import ipdb @@ -1211,7 +1237,9 @@ def step(self, action: Tuple[float]) -> Tuple[List[float], Tuple[int, float], bo if self.done: return self.state, action, False if action == self.eos: - assert self.state != self.source + assert self._get_effective_dims(self.state) != self._get_effective_dims( + self.source + ) self.done = True self.n_actions += 1 return self.state, self.eos, True From c20f2ecd2ac430055723e54740452a0d66e140c9 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 18 Sep 2023 16:49:22 -0400 Subject: [PATCH 011/205] Add not equality constraints of lengfths in triclinic. --- tests/gflownet/envs/test_clattice_parameters.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/gflownet/envs/test_clattice_parameters.py b/tests/gflownet/envs/test_clattice_parameters.py index 17a9e8592..cfbcd9f5e 100644 --- a/tests/gflownet/envs/test_clattice_parameters.py +++ b/tests/gflownet/envs/test_clattice_parameters.py @@ -164,9 +164,11 @@ def test__tetragonal__constraints_remain_after_random_actions(env, lattice_syste def test__triclinic__constraints_remain_after_random_actions(env, lattice_system): env = env.reset() while not env.done: - # TODO: Test not equality constraints env.step_random() (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert a != b + assert a != c + assert b != c assert len({alpha, beta, gamma, 90.0}) == 4 From a08417e5bbba19d787363b6c1c9a2e75154df50f Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 18 Sep 2023 16:50:11 -0400 Subject: [PATCH 012/205] New version of CLatticeParameters env which uses the new functionality of the Cube about ignored dimensions. --- gflownet/envs/crystals/clattice_parameters.py | 275 +++++++++++++++++- 1 file changed, 263 insertions(+), 12 deletions(-) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index 3c4e75e3d..279262fc1 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -22,6 +22,10 @@ TRICLINIC, ) +LENGTHS = ("a", "b", "c") +ANGLES = ("alpha", "beta", "gamma") +PARAMETERS = LENGTHS + ANGLES + # TODO: figure out a way to inherit the (discrete) LatticeParameters env or create a # common class for both discrete and continous with the common methods. @@ -35,9 +39,250 @@ class CLatticeParameters(ContinuousCube): from the (continuous) cube environment, creating a mapping between cell position and edge length or angle, and imposing lattice system constraints on their values. - Similar to the Cube environment, the values are initialized with zeros - (or target angles, if they are predetermined by the lattice system), and are - incremented by sampling from a (mixture of) Beta distribution(s). + The environment is a hyper cube of dimensionality 6 (the number of lattice + parameters), but it takes advantage of the mask of ignored dimensions implemented + in the Cube environment. + + The values of the state will remain in the default [0, 1] range of the Cube, but + they are mapped to [min_length, max_length] in the case of the lengths and + [min_angle, max_angle] in the case of the angles. + """ + + def __init__( + self, + lattice_system: str, + min_length: float = 1.0, + max_length: float = 5.0, + min_angle: float = 30.0, + max_angle: float = 150.0, + **kwargs, + ): + """ + Args + ---- + lattice_system : str + One of the seven lattice systems. + + min_length : float + Minimum value of the lengths. + + max_length : float + Maximum value of the lengths. + + min_angle : float + Minimum value of the angles. + + max_angle : float + Maximum value of the angles. + """ + self.lattice_system = lattice_system + self.min_length = min_length + self.max_length = max_length + self.length_range = self.max_length - self.min_length + self.min_angle = min_angle + self.max_angle = max_angle + self.angle_range = self.max_angle - self.min_angle + self._setup_constraints() + super().__init__(n_dim=6, **kwargs) + + def _statevalue2length(self, value): + return self.min_length + value * self.length_range + + def _length2statevalue(self, length): + return (length - self.min_length) / self.length_range + + def _statevalue2angle(self, value): + return self.min_angle + value * self.angle_range + + def _angle2statevalue(self, angle): + return (angle - self.min_angle) / self.angle_range + + def _get_param(self, param): + if hasattr(self, param): + return getattr(self, param) + else: + if param in LENGTHS: + return self._statevalue2length( + self.state[self._get_index_of_param(param)] + ) + elif param in ANGLES: + return self._statevalue2angle( + self.state[self._get_index_of_param(param)] + ) + else: + raise ValueError(f"{param} is not a valid lattice parameter") + + def _set_param(self, state, param, value): + param_idx = self._get_index_of_param(param) + if param_idx is not None: + if param in LENGTHS: + state[param_idx] = self._length2statevalue(value) + elif param in ANGLES: + state[param_idx] = self._angle2statevalue(value) + else: + raise ValueError(f"{param} is not a valid lattice parameter") + return state + + def _get_index_of_param(self, param): + param_idx = f"{param}_idx" + if hasattr(self, param_idx): + return getattr(self, param_idx) + else: + return None + + def _setup_constraints(self): + """ + Computes the mask of ignored dimensions, given the constraints imposed by the + lattice system. Sets self.ignored_dims. + """ + # Lengths: a, b, c + # a == b == c + if self.lattice_system in [CUBIC, RHOMBOHEDRAL]: + lengths_ignored_dims = [False, True, True] + self.a_idx = 0 + self.b_idx = 0 + self.c_idx = 0 + # a == b != c + elif self.lattice_system in [HEXAGONAL, TETRAGONAL]: + lengths_ignored_dims = [False, True, False] + self.a_idx = 0 + self.b_idx = 0 + self.c_idx = 1 + # a != b and a != c and b != c + elif self.lattice_system in [MONOCLINIC, ORTHORHOMBIC, TRICLINIC]: + lengths_ignored_dims = [False, False, False] + self.a_idx = 0 + self.b_idx = 1 + self.c_idx = 2 + else: + raise NotImplementedError + # Angles: alpha, beta, gamma + # alpha == beta == gamma == 90.0 + if self.lattice_system in [CUBIC, ORTHORHOMBIC, TETRAGONAL]: + angles_ignored_dims = [True, True, True] + self.alpha_idx = None + self.alpha = 90.0 + self.alpha_state = self._angle2statevalue(self.alpha) + self.beta_idx = None + self.beta = 90.0 + self.beta_state = self._angle2statevalue(self.beta) + self.gamma_idx = None + self.gamma = 90.0 + self.gamma_state = self._angle2statevalue(self.gamma) + # alpha == beta == 90.0 and gamma == 120.0 + elif self.lattice_system == HEXAGONAL: + angles_ignored_dims = [True, True, True] + self.alpha_idx = None + self.alpha = 90.0 + self.alpha_state = self._angle2statevalue(self.alpha) + self.beta_idx = None + self.beta = 90.0 + self.beta_state = self._angle2statevalue(self.beta) + self.gamma_idx = None + self.gamma = 120.0 + self.gamma_state = self._angle2statevalue(self.gamma) + # alpha == gamma == 90.0 and beta != 90.0 + elif self.lattice_system == MONOCLINIC: + angles_ignored_dims = [True, False, True] + self.alpha_idx = None + self.alpha = 90.0 + self.alpha_state = self._angle2statevalue(self.alpha) + self.beta_idx = 4 + self.gamma_idx = None + self.gamma = 90.0 + self.gamma_state = self._angle2statevalue(self.gamma) + # alpha == beta == gamma != 90.0 + elif self.lattice_system == RHOMBOHEDRAL: + angles_ignored_dims = [False, True, True] + self.alpha_idx = 3 + self.beta_idx = 3 + self.gamma_idx = 3 + # alpha != beta, alpha != gamma, beta != gamma + elif self.lattice_system == TRICLINIC: + angles_ignored_dims = [False, False, False] + self.alpha_idx = 3 + self.beta_idx = 4 + self.gamma_idx = 5 + else: + raise NotImplementedError + self.ignored_dims = lengths_ignored_dims + angles_ignored_dims + + def _step( + self, + action: Tuple[float], + backward: bool, + ) -> Tuple[List[float], Tuple[float], bool]: + """ + Updates the dimensions of the state corresponding to the ignored dimensions + after a call to the Cube's _step(). + """ + state, action, valid = super()._step(action, backward) + for idx, (param, is_ignored) in enumerate(zip(PARAMETERS, self.ignored_dims)): + if not is_ignored: + continue + param_idx = self._get_index_of_param(param) + if param_idx is not None: + state[idx] = state[param_idx] + else: + state[idx] = getattr(self, f"{param}_state") + self.state = copy(state) + return self.state, action, valid + + def _unpack_lengths_angles( + self, state: Optional[List[int]] = None + ) -> Tuple[Tuple, Tuple]: + """ + Helper that 1) unpacks values coding lengths and angles from the state or from + the attributes of the instance and 2) converts them to actual edge lengths and + angles in the target units (angstroms or degrees). + """ + state = self._get_state(state) + + a, b, c, alpha, beta, gamma = [self._get_param(p) for p in PARAMETERS] + return (a, b, c), (alpha, beta, gamma) + + def state2readable(self, state: Optional[List[int]] = None) -> str: + """ + Converts the state into a human-readable string in the format "(a, b, c), + (alpha, beta, gamma)". + """ + state = self._get_state(state) + + lengths, angles = self._unpack_lengths_angles(state) + return f"{lengths}, {angles}" + + def readable2state(self, readable: str) -> List[int]: + """ + Converts a human-readable representation of a state into the standard format. + """ + state = copy(self.source) + + for c in ["(", ")", " "]: + readable = readable.replace(c, "") + values = readable.split(",") + values = [float(value) for value in values] + + for param, value in zip(PARAMETERS, values): + state = self._set_param(state, param, value) + return state + + +# TODO: figure out a way to inherit the (discrete) LatticeParameters env or create a +# common class for both discrete and continous with the common methods. +class CLatticeParametersEffectiveDim(ContinuousCube): + """ + Continuous lattice parameters environment for crystal structures generation. + + Models lattice parameters (three edge lengths and three angles describing unit + cell) with the constraints given by the provided lattice system (see + https://en.wikipedia.org/wiki/Bravais_lattice). This is implemented by inheriting + from the (continuous) cube environment, creating a mapping between cell position + and edge length or angle, and imposing lattice system constraints on their values. + + The environment is simply a hyper cube with a number of dimensions equal to the the + number of "effective dimensions". While this is nice and simple, it does not allow + us to integrate it in the general crystals such that lattices of any lattice system + can be sampled. The values of the state will remain in the default [0, 1] range of the Cube, but they are mapped to [min_length, max_length] in the case of the lengths and @@ -71,9 +316,6 @@ def __init__( max_angle : float Maximum value of the angles. """ - self.lengths = ("a", "b", "c") - self.angles = ("alpha", "beta", "gamma") - self.parameters = self.lengths + self.angles self.lattice_system = lattice_system self.min_length = min_length self.max_length = max_length @@ -97,14 +339,18 @@ def _angle2statevalue(self, angle): return (angle - self.min_angle) / self.angle_range def _get_param(self, param): + """ + Returns the value of parameter param (a, b, c, alpha, beta, gamma) in the + target units (angstroms or degrees). + """ if hasattr(self, param): return getattr(self, param) else: - if param in self.lengths: + if param in LENGTHS: return self._statevalue2length( self.state[self._get_index_of_param(param)] ) - elif param in self.angles: + elif param in ANGLES: return self._statevalue2angle( self.state[self._get_index_of_param(param)] ) @@ -112,11 +358,16 @@ def _get_param(self, param): raise ValueError(f"{param} is not a valid lattice parameter") def _set_param(self, state, param, value): + """ + Sets the value of parameter param (a, b, c, alpha, beta, gamma) given in target + units (angstroms or degrees) in the state, after conversion to state units in + [0, 1]. + """ param_idx = self._get_index_of_param(param) if param_idx: - if param in self.lengths: + if param in LENGTHS: state[param_idx] = self._length2statevalue(value) - elif param in self.angles: + elif param in ANGLES: state[param_idx] = self._angle2statevalue(value) else: raise ValueError(f"{param} is not a valid lattice parameter") @@ -213,7 +464,7 @@ def _unpack_lengths_angles( """ state = self._get_state(state) - a, b, c, alpha, beta, gamma = [self._get_param(p) for p in self.parameters] + a, b, c, alpha, beta, gamma = [self._get_param(p) for p in PARAMETERS] return (a, b, c), (alpha, beta, gamma) def state2readable(self, state: Optional[List[int]] = None) -> str: @@ -237,6 +488,6 @@ def readable2state(self, readable: str) -> List[int]: values = readable.split(",") values = [float(value) for value in values] - for param, value in zip(self.parameters, values): + for param, value in zip(PARAMETERS, values): state = self._set_param(state, param, value) return state From 1441cbf450166d008707556e318ccc617eee022c Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 18 Sep 2023 16:57:19 -0400 Subject: [PATCH 013/205] Set continuous = True in environment init, not in config. --- config/env/crystals/clattice_parameters.yaml | 1 - gflownet/envs/crystals/clattice_parameters.py | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/config/env/crystals/clattice_parameters.yaml b/config/env/crystals/clattice_parameters.yaml index 084a44a6f..ae6d83293 100644 --- a/config/env/crystals/clattice_parameters.yaml +++ b/config/env/crystals/clattice_parameters.yaml @@ -4,7 +4,6 @@ defaults: _target_: gflownet.envs.crystals.clattice_parameters.CLatticeParameters id: clattice_parameters -continuous: True # Lattice system lattice_system: triclinic diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index 279262fc1..875f1e653 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -75,6 +75,7 @@ def __init__( max_angle : float Maximum value of the angles. """ + self.continuous = True self.lattice_system = lattice_system self.min_length = min_length self.max_length = max_length From 4ffded26a56fab6e237cb7ac39dac0d023c43d29 Mon Sep 17 00:00:00 2001 From: Alex Date: Mon, 18 Sep 2023 22:59:58 +0200 Subject: [PATCH 014/205] Fix typo MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Michał Koziarski --- gflownet/envs/crystals/clattice_parameters.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index 875f1e653..39455424f 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -389,7 +389,7 @@ def _setup_constraints(self): Returns ------- n_dim : int - The number of effective dimensions that can be be udpated in the + The number of effective dimensions that can be be updated in the environment, given the constraints set by the lattice system. """ # Lengths: a, b, c From 2e9a1b3127152d544c8ca6e407e4bb77ab46f639 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 16:31:30 -0400 Subject: [PATCH 015/205] Rename variables. --- gflownet/envs/crystals/clattice_parameters.py | 34 ++++++++++--------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index 875f1e653..7c060a408 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -22,9 +22,9 @@ TRICLINIC, ) -LENGTHS = ("a", "b", "c") -ANGLES = ("alpha", "beta", "gamma") -PARAMETERS = LENGTHS + ANGLES +LENGTH_PARAMETER_NAMES = ("a", "b", "c") +ANGLE_PARAMETER_NAMES = ("alpha", "beta", "gamma") +PARAMETER_NAMES = LENGTH_PARAMETER_NAMES + ANGLE_PARAMETER_NAMES # TODO: figure out a way to inherit the (discrete) LatticeParameters env or create a @@ -102,11 +102,11 @@ def _get_param(self, param): if hasattr(self, param): return getattr(self, param) else: - if param in LENGTHS: + if param in LENGTH_PARAMETER_NAMES: return self._statevalue2length( self.state[self._get_index_of_param(param)] ) - elif param in ANGLES: + elif param in ANGLE_PARAMETER_NAMES: return self._statevalue2angle( self.state[self._get_index_of_param(param)] ) @@ -116,9 +116,9 @@ def _get_param(self, param): def _set_param(self, state, param, value): param_idx = self._get_index_of_param(param) if param_idx is not None: - if param in LENGTHS: + if param in LENGTH_PARAMETER_NAMES: state[param_idx] = self._length2statevalue(value) - elif param in ANGLES: + elif param in ANGLE_PARAMETER_NAMES: state[param_idx] = self._angle2statevalue(value) else: raise ValueError(f"{param} is not a valid lattice parameter") @@ -218,7 +218,9 @@ def _step( after a call to the Cube's _step(). """ state, action, valid = super()._step(action, backward) - for idx, (param, is_ignored) in enumerate(zip(PARAMETERS, self.ignored_dims)): + for idx, (param, is_ignored) in enumerate( + zip(PARAMETER_NAMES, self.ignored_dims) + ): if not is_ignored: continue param_idx = self._get_index_of_param(param) @@ -239,7 +241,7 @@ def _unpack_lengths_angles( """ state = self._get_state(state) - a, b, c, alpha, beta, gamma = [self._get_param(p) for p in PARAMETERS] + a, b, c, alpha, beta, gamma = [self._get_param(p) for p in PARAMETER_NAMES] return (a, b, c), (alpha, beta, gamma) def state2readable(self, state: Optional[List[int]] = None) -> str: @@ -263,7 +265,7 @@ def readable2state(self, readable: str) -> List[int]: values = readable.split(",") values = [float(value) for value in values] - for param, value in zip(PARAMETERS, values): + for param, value in zip(PARAMETER_NAMES, values): state = self._set_param(state, param, value) return state @@ -347,11 +349,11 @@ def _get_param(self, param): if hasattr(self, param): return getattr(self, param) else: - if param in LENGTHS: + if param in LENGTH_PARAMETER_NAMES: return self._statevalue2length( self.state[self._get_index_of_param(param)] ) - elif param in ANGLES: + elif param in ANGLE_PARAMETER_NAMES: return self._statevalue2angle( self.state[self._get_index_of_param(param)] ) @@ -366,9 +368,9 @@ def _set_param(self, state, param, value): """ param_idx = self._get_index_of_param(param) if param_idx: - if param in LENGTHS: + if param in LENGTH_PARAMETER_NAMES: state[param_idx] = self._length2statevalue(value) - elif param in ANGLES: + elif param in ANGLE_PARAMETER_NAMES: state[param_idx] = self._angle2statevalue(value) else: raise ValueError(f"{param} is not a valid lattice parameter") @@ -465,7 +467,7 @@ def _unpack_lengths_angles( """ state = self._get_state(state) - a, b, c, alpha, beta, gamma = [self._get_param(p) for p in PARAMETERS] + a, b, c, alpha, beta, gamma = [self._get_param(p) for p in PARAMETER_NAMES] return (a, b, c), (alpha, beta, gamma) def state2readable(self, state: Optional[List[int]] = None) -> str: @@ -489,6 +491,6 @@ def readable2state(self, readable: str) -> List[int]: values = readable.split(",") values = [float(value) for value in values] - for param, value in zip(PARAMETERS, values): + for param, value in zip(PARAMETER_NAMES, values): state = self._set_param(state, param, value) return state From ceb5788c3adcb2b6b75eaadc4ca073ecf5850980 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 17:28:13 -0400 Subject: [PATCH 016/205] Extend tests of constraints and improve consistency. --- .../gflownet/envs/test_clattice_parameters.py | 52 +++++++------------ 1 file changed, 20 insertions(+), 32 deletions(-) diff --git a/tests/gflownet/envs/test_clattice_parameters.py b/tests/gflownet/envs/test_clattice_parameters.py index cfbcd9f5e..40182d5b7 100644 --- a/tests/gflownet/envs/test_clattice_parameters.py +++ b/tests/gflownet/envs/test_clattice_parameters.py @@ -14,7 +14,7 @@ CLatticeParameters, ) -N_REPETITIONS = 100 +N_REPETITIONS = 1000 @pytest.fixture() @@ -66,12 +66,8 @@ def test__cubic__constraints_remain_after_random_actions(env, lattice_system): env = env.reset() while not env.done: (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() - assert a == b - assert b == c - assert a == c - assert alpha == 90.0 - assert beta == 90.0 - assert gamma == 90.0 + assert len({a, b, c}) == 1 + assert len({alpha, beta, gamma, 90.0}) == 1 env.step_random() @@ -83,12 +79,12 @@ def test__cubic__constraints_remain_after_random_actions(env, lattice_system): def test__hexagonal__constraints_remain_after_random_actions(env, lattice_system): env = env.reset() while not env.done: + env.step_random() (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() assert a == b - assert alpha == 90.0 - assert beta == 90.0 + assert len({a, b, c}) == 2 + assert len({alpha, beta, 90.0}) == 1 assert gamma == 120.0 - env.step_random() @pytest.mark.parametrize( @@ -99,11 +95,11 @@ def test__hexagonal__constraints_remain_after_random_actions(env, lattice_system def test__monoclinic__constraints_remain_after_random_actions(env, lattice_system): env = env.reset() while not env.done: + env.step_random() (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() - assert alpha == 90.0 + assert len({a, b, c}) == 3 + assert len({alpha, gamma, 90.0}) == 1 assert beta != 90.0 - assert gamma == 90.0 - env.step_random() @pytest.mark.parametrize( @@ -114,11 +110,10 @@ def test__monoclinic__constraints_remain_after_random_actions(env, lattice_syste def test__orthorhombic__constraints_remain_after_random_actions(env, lattice_system): env = env.reset() while not env.done: - (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() - assert alpha == 90.0 - assert beta == 90.0 - assert gamma == 90.0 env.step_random() + (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert len({a, b, c}) == 3 + assert len({alpha, beta, gamma, 90.0}) == 1 @pytest.mark.parametrize( @@ -129,15 +124,11 @@ def test__orthorhombic__constraints_remain_after_random_actions(env, lattice_sys def test__rhombohedral__constraints_remain_after_random_actions(env, lattice_system): env = env.reset() while not env.done: - (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() - assert a == b - assert b == c - assert a == c - assert alpha == beta - assert beta == gamma - assert alpha == gamma - assert alpha != 90.0 env.step_random() + (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() + assert len({a, b, c}) == 1 + assert len({alpha, beta, gamma}) == 1 + assert len({alpha, beta, gamma, 90.0}) == 2 @pytest.mark.parametrize( @@ -148,12 +139,11 @@ def test__rhombohedral__constraints_remain_after_random_actions(env, lattice_sys def test__tetragonal__constraints_remain_after_random_actions(env, lattice_system): env = env.reset() while not env.done: + env.step_random() (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() assert a == b - assert alpha == 90.0 - assert beta == 90.0 - assert gamma == 90.0 - env.step_random() + assert len({a, b, c}) == 2 + assert len({alpha, beta, gamma, 90.0}) == 1 @pytest.mark.parametrize( @@ -166,9 +156,7 @@ def test__triclinic__constraints_remain_after_random_actions(env, lattice_system while not env.done: env.step_random() (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() - assert a != b - assert a != c - assert b != c + assert len({a, b, c}) == 3 assert len({alpha, beta, gamma, 90.0}) == 4 From 3ee57b12c87a8b71ab355c9134e7d1e691173ce5 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 17:31:01 -0400 Subject: [PATCH 017/205] Fix test method name. --- tests/gflownet/envs/test_clattice_parameters.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/gflownet/envs/test_clattice_parameters.py b/tests/gflownet/envs/test_clattice_parameters.py index 40182d5b7..6c6ee55ee 100644 --- a/tests/gflownet/envs/test_clattice_parameters.py +++ b/tests/gflownet/envs/test_clattice_parameters.py @@ -190,7 +190,7 @@ def test__state2readable__gives_expected_results_for_initial_states( (TRICLINIC, "(1.0, 1.0, 1.0), (30.0, 30.0, 30.0)"), ], ) -def test__readable2state__returns_initial_state_for_rhombohedral_and_triclinic( +def test__readable2state__gives_expected_results_for_initial_states( env, lattice_system, readable ): assert env.readable2state(readable) == env.state From d6303ec7490aa35e74213adfb7346f4b315b39ea Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 17:38:06 -0400 Subject: [PATCH 018/205] Remove unused variables --- gflownet/envs/cube.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 89d070e80..9791d90e1 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -808,8 +808,6 @@ def _sample_actions_batch_backward( # Initialize variables n_states = policy_outputs.shape[0] is_bts = torch.zeros(n_states, dtype=torch.bool, device=self.device) - # Mask of effective dimensions - is_effective_dim = ~mask[:, -self.n_dim :] # EOS is the only possible action only if done is True (mask[2] is False) is_eos = ~mask[:, 2] # Back-to-source (BTS) is the only possible action if mask[1] is False @@ -938,8 +936,6 @@ def _get_logprobs_forward( (n_states, self.n_dim), device=self.device, dtype=self.float ) eos_tensor = tfloat(self.eos, float_type=self.float, device=self.device) - # Mask of effective dimensions - is_effective_dim = ~mask[:, -self.n_dim :] # Determine source states is_source = ~mask[:, 1] # EOS is the only possible action if continuous actions are invalid (mask[0] is @@ -1031,8 +1027,6 @@ def _get_logprobs_backward( log_jacobian_diag = torch.zeros( (n_states, self.n_dim), device=self.device, dtype=self.float ) - # Mask of effective dimensions - is_effective_dim = ~mask[:, -self.n_dim :] # EOS is the only possible action only if done is True (mask[2] is False) is_eos = ~mask[:, 2] # Back-to-source (BTS) is the only possible action if mask[1] is False From 7b653c86eea1903226e78239d48545297c0b39d4 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 20:40:26 -0400 Subject: [PATCH 019/205] Fix merging issue in get_logprobs --- gflownet/envs/cube.py | 44 ++++++++++++++----------------------------- 1 file changed, 14 insertions(+), 30 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index fc7c9dca2..565a8e90e 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -64,8 +64,6 @@ def __init__( epsilon: float = 1e-6, kappa: float = 1e-3, ignored_dims: Optional[List[bool]] = None, - beta_params_min: float = 0.1, - beta_params_max: float = 1000.0, fixed_distr_params: dict = { "beta_params_min": 0.1, "beta_params_max": 1000.0, @@ -1065,10 +1063,14 @@ def _get_logprobs_forward( # not source is_relative = torch.logical_and(do_increments, ~is_source) if torch.any(is_relative): - jacobian_diag[is_relative] = self._get_jacobian_diag( - states_from_rel, - is_backward=False, + log_jacobian_diag[is_relative] = torch.log( + self._get_jacobian_diag( + states_from_rel, + is_backward=False, + ) ) + # Make ignored dimensions zero + log_jacobian_diag = self._mask_ignored_dimensions(mask, log_jacobian_diag) # Get logprobs distr_increments = self._make_increments_distribution( policy_outputs[do_increments] @@ -1081,17 +1083,6 @@ def _get_logprobs_forward( logprobs_increments_rel = self._mask_ignored_dimensions( mask, logprobs_increments_rel ) - # Compute log of the diagonal of the Jacobian (see _get_jacobian_diag()) - log_jacobian_diag[do_increments] = torch.log( - self._get_jacobian_diag( - states_from_tensor[do_increments], - min_increments, - self.max_val, - is_backward=False, - ) - ) - # Make ignored dimensions zero - log_jacobian_diag = self._mask_ignored_dimensions(mask, log_jacobian_diag) # Sum log Jacobian across dimensions log_det_jacobian = torch.sum(log_jacobian_diag, dim=1) # Compute combined probabilities @@ -1155,10 +1146,14 @@ def _get_logprobs_backward( is_backward=True, ) # Compute diagonal of the Jacobian (see _get_jacobian_diag()) - jacobian_diag[do_increments] = self._get_jacobian_diag( - states_from_tensor[do_increments], - is_backward=True, + log_jacobian_diag[do_increments] = torch.log( + self._get_jacobian_diag( + states_from_tensor[do_increments], + is_backward=True, + ) ) + # Make ignored dimensions zero + log_jacobian_diag = self._mask_ignored_dimensions(mask, log_jacobian_diag) # Get logprobs distr_increments = self._make_increments_distribution( policy_outputs[do_increments] @@ -1171,17 +1166,6 @@ def _get_logprobs_backward( logprobs_increments_rel = self._mask_ignored_dimensions( mask, logprobs_increments_rel ) - # Compute log of the diagonal of the Jacobian (see _get_jacobian_diag()) - log_jacobian_diag[do_increments] = torch.log( - self._get_jacobian_diag( - states_from_tensor[do_increments], - min_increments, - self.max_val, - is_backward=True, - ) - ) - # Make ignored dimensions zero - log_jacobian_diag = self._mask_ignored_dimensions(mask, log_jacobian_diag) # Sum log Jacobian across dimensions log_det_jacobian = torch.sum(log_jacobian_diag, dim=1) # Compute combined probabilities From 6d4ca9e437822af330943f7e80e566e24069d341 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 20:47:26 -0400 Subject: [PATCH 020/205] Fixes of merging issues in tests - partial --- tests/gflownet/envs/test_ccube.py | 2184 ++++++++++++++--------------- 1 file changed, 1092 insertions(+), 1092 deletions(-) diff --git a/tests/gflownet/envs/test_ccube.py b/tests/gflownet/envs/test_ccube.py index 6f787289e..aee2fe1fa 100644 --- a/tests/gflownet/envs/test_ccube.py +++ b/tests/gflownet/envs/test_ccube.py @@ -64,1095 +64,1095 @@ def policy_output__as_expected(env, policy_outputs, params): ) -@pytest.mark.parametrize("env", ["cube1d", "cube2d"]) -def test__mask_forward__returns_all_true_if_done(env, request): - env = request.getfixturevalue(env) - # Sample states - states = env.get_uniform_terminating_states(100) - # Iterate over state and test - for state in states: - env.set_state(state, done=True) - mask = env.get_mask_invalid_actions_forward() - assert all(mask) - - -@pytest.mark.parametrize("env", ["cube1d", "cube2d"]) -def test__mask_backward__returns_all_true_except_eos_if_done(env, request): - env = request.getfixturevalue(env) - # Sample states - states = env.get_uniform_terminating_states(100) - # Iterate over state and test - for state in states: - env.set_state(state, done=True) - mask = env.get_mask_invalid_actions_backward() - assert all(mask[:2]) - assert mask[2] is False - - -@pytest.mark.parametrize( - "state, mask_expected", - [ - ( - [-1.0], - [False, False, True, False], - ), - ( - [0.0], - [False, True, False, False], - ), - ( - [0.5], - [False, True, False, False], - ), - ( - [0.90], - [False, True, False, False], - ), - ( - [0.95], - [True, True, False, False], - ), - ], -) -def test__mask_forward__1d__returns_expected(cube1d, state, mask_expected): - env = cube1d - mask = env.get_mask_invalid_actions_forward(state) - assert mask == mask_expected - - -@pytest.mark.parametrize( - "state, mask_expected", - [ - ( - [-1.0, -1.0], - [False, False, True, False, False], - ), - ( - [0.0, 0.0], - [False, True, False], - ), - ( - [0.5, 0.0], - [False, True, False], - ), - ( - [0.0, 0.01], - [False, True, False], - ), - ( - [0.5, 0.5], - [False, True, False, False, False], - ), - ( - [0.90, 0.5], - [False, True, False, False, False], - ), - ( - [0.95, 0.5], - [True, True, False, False, False], - ), - ( - [0.5, 0.90], - [False, True, False, False, False], - ), - ( - [0.5, 0.95], - [True, True, False, False, False], - ), - ( - [0.95, 0.95], - [True, True, False, False, False], - ), - ], -) -def test__mask_forward__2d__returns_expected(cube2d, state, mask_expected): - env = cube2d - mask = env.get_mask_invalid_actions_forward(state) - assert mask == mask_expected - - -@pytest.mark.parametrize( - "state, mask_expected", - [ - ( - [-1.0], - [True, False, True, False], - ), - ( - [0.0], - [True, False, True, False], - ), - ( - [0.05], - [True, False, True, False], - ), - ( - [0.1], - [False, True, True], - ), - ( - [0.5], - [False, True, True, False], - ), - ( - [0.90], - [False, True, True, False], - ), - ( - [0.95], - [False, True, True, False], - ), - ], -) -def test__mask_backward__1d__returns_expected(cube1d, state, mask_expected): - env = cube1d - mask = env.get_mask_invalid_actions_backward(state) - assert mask == mask_expected - - -@pytest.mark.parametrize( - "state, mask_expected", - [ - ( - [-1.0, -1.0], - [True, True, True], - ), - ( - [0.0, 0.0], - [True, False, True, False, False], - ), - ( - [0.5, 0.5], - [False, True, True, False, False], - ), - ( - [0.05, 0.5], - [True, False, True, False, False], - ), - ( - [0.5, 0.05], - [True, False, True, False, False], - ), - ( - [0.05, 0.05], - [True, False, True, False, False], - ), - ( - [0.90, 0.5], - [False, True, True, False, False], - ), - ( - [0.5, 0.90], - [False, True, True, False, False], - ), - ( - [0.95, 0.5], - [False, True, True, False, False], - ), - ( - [0.5, 0.95], - [False, True, True, False, False], - ), - ( - [0.95, 0.95], - [False, True, True, False, False], - ), - ], -) -def test__mask_backward__2d__returns_expected(cube2d, state, mask_expected): - env = cube2d - mask = env.get_mask_invalid_actions_backward(state) - assert mask == mask_expected - - -@pytest.mark.parametrize( - "state, increments_rel, state_expected", - [ - ( - [0.3, 0.5], - [0.0, 0.0], - [0.4, 0.6], - ), - ( - [0.0, 0.0], - [0.1794, 0.9589], - [0.26146, 0.96301], - ), - ( - [0.3, 0.5], - [1.0, 1.0], - [1.0, 1.0], - ), - ( - [0.3, 0.5], - [0.5, 0.5], - [0.7, 0.8], - ), - ( - [0.27, 0.85], - [0.12, 0.76], - [0.4456, 0.988], - ), - ], -) -def test__relative_to_absolute_increments__2d_forward__returns_expected( - cube2d, state, increments_rel, state_expected -): - env = cube2d - # Convert to tensors - states = tfloat([state], float_type=env.float, device=env.device) - increments_rel = tfloat([increments_rel], float_type=env.float, device=env.device) - states_expected = tfloat([state_expected], float_type=env.float, device=env.device) - # Get absolute increments - increments_abs = env.relative_to_absolute_increments( - states, increments_rel, is_backward=False - ) - states_next = states + increments_abs - assert torch.all(torch.isclose(states_next, states_expected)) - - -@pytest.mark.parametrize( - "state, increments_rel, state_expected", - [ - ( - [1.0, 1.0], - [0.0, 0.0], - [0.9, 0.9], - ), - ( - [1.0, 1.0], - [1.0, 1.0], - [0.0, 0.0], - ), - ( - [1.0, 1.0], - [0.1794, 0.9589], - [0.73854, 0.03699], - ), - ( - [0.3, 0.5], - [0.0, 0.0], - [0.2, 0.4], - ), - ( - [0.3, 0.5], - [1.0, 1.0], - [0.0, 0.0], - ), - ], -) -def test__relative_to_absolute_increments__2d_backward__returns_expected( - cube2d, state, increments_rel, state_expected -): - env = cube2d - # Convert to tensors - states = tfloat([state], float_type=env.float, device=env.device) - increments_rel = tfloat([increments_rel], float_type=env.float, device=env.device) - states_expected = tfloat([state_expected], float_type=env.float, device=env.device) - # Get absolute increments - increments_abs = env.relative_to_absolute_increments( - states, increments_rel, is_backward=True - ) - states_next = states - increments_abs - assert torch.all(torch.isclose(states_next, states_expected)) - - -@pytest.mark.parametrize( - "state, action, state_expected", - [ - ( - [-1.0, -1.0], - (0.5, 0.5, 1.0), - [0.5, 0.5], - ), - ( - [-1.0, -1.0], - (0.0, 0.0, 1.0), - [0.0, 0.0], - ), - ( - [-1.0, -1.0], - (0.1794, 0.9589, 1.0), - [0.1794, 0.9589], - ), - ( - [0.0, 0.0], - (0.1, 0.1, 0.0), - [0.1, 0.1], - ), - ( - [0.0, 0.0], - (0.1794, 0.9589, 0.0), - [0.1794, 0.9589], - ), - ( - [0.3, 0.5], - (0.1, 0.1, 0.0), - [0.4, 0.6], - ), - ( - [0.3, 0.5], - (0.7, 0.5, 0.0), - [1.0, 1.0], - ), - ( - [0.3, 0.5], - (0.4, 0.3, 0.0), - [0.7, 0.8], - ), - ( - [0.27, 0.85], - (0.1756, 0.138, 0.0), - [0.4456, 0.988], - ), - ( - [0.45, 0.27], - (np.inf, np.inf, np.inf), - [0.45, 0.27], - ), - ( - [0.0, 0.0], - (np.inf, np.inf, np.inf), - [0.0, 0.0], - ), - ], -) -def test__step_forward__2d__returns_expected(cube2d, state, action, state_expected): - env = cube2d - env.set_state(state) - state_new, action, valid = env.step(action) - assert env.isclose(state_new, state_expected) - - -@pytest.mark.parametrize( - "state, action, state_expected", - [ - ( - [0.5, 0.9], - (0.3, 0.2, 0.0), - [0.2, 0.7], - ), - ( - [0.95, 0.4456], - (0.1, 0.27, 0.0), - [0.85, 0.1756], - ), - ( - [0.1, 0.2], - (0.1, 0.1, 0.0), - [0.0, 0.1], - ), - ( - [0.1, 0.2], - (0.1, 0.2, 1.0), - [-1.0, -1.0], - ), - ( - [0.95, 0.0], - (0.95, 0.0, 1.0), - [-1.0, -1.0], - ), - ], -) -def test__step_backward__2d__returns_expected(cube2d, state, action, state_expected): - env = cube2d - env.set_state(state) - state_new, action, valid = env.step_backwards(action) - assert env.isclose(state_new, state_expected) - - -@pytest.mark.parametrize( - "states, force_eos", - [ - ( - [[-1.0, -1.0], [0.0, 0.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], - [False, False, False, False, False], - ), - ( - [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.0], [0.16, 0.93]], - [False, False, False, False, False], - ), - ( - [[0.05, 0.97], [0.56, 0.23], [0.95, 0.3], [0.2, 0.95], [0.01, 0.01]], - [False, False, False, False, False], - ), - ( - [[0.0, 0.0], [0.0, 0.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], - [False, False, False, True, False], - ), - ( - [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.0], [0.16, 0.93]], - [False, True, True, False, False], - ), - ( - [[0.05, 0.97], [0.56, 0.23], [0.95, 0.98], [0.92, 0.95], [0.01, 0.01]], - [False, False, False, True, True], - ), - ], -) -def test__sample_actions_forward__2d__returns_expected(cube2d, states, force_eos): - env = cube2d - n_states = len(states) - force_eos = tbool(force_eos, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device - ) - # Define Beta distribution with low variance and get confident range - n_samples = 10000 - beta_params_min = 0.0 - beta_params_max = 10000 - alpha = 10 - alphas_presigmoid = alpha * torch.ones(n_samples) - alphas = beta_params_max * torch.sigmoid(alphas_presigmoid) + beta_params_min - beta = 1.0 - betas_presigmoid = beta * torch.ones(n_samples) - betas = beta_params_max * torch.sigmoid(betas_presigmoid) + beta_params_min - beta_distr = Beta(alphas, betas) - samples = beta_distr.sample() - mean_incr_rel = 0.9 * samples.mean() - min_incr_rel = 0.9 * samples.min() - max_incr_rel = 1.1 * samples.max() - # Define Bernoulli parameters for EOS with deterministic probability - logit_force_eos = torch.inf - logit_force_noeos = -torch.inf - # Estimate confident intervals of absolute actions - states_torch = tfloat(states, float_type=env.float, device=env.device) - is_source = torch.all(states_torch == -1.0, dim=1) - is_near_edge = states_torch > 1.0 - env.min_incr - increments_min = torch.full_like( - states_torch, min_incr_rel, dtype=env.float, device=env.device - ) - increments_max = torch.full_like( - states_torch, max_incr_rel, dtype=env.float, device=env.device - ) - increments_min[~is_source] = env.relative_to_absolute_increments( - states_torch[~is_source], increments_min[~is_source], is_backward=False - ) - increments_max[~is_source] = env.relative_to_absolute_increments( - states_torch[~is_source], increments_max[~is_source], is_backward=False - ) - # Get EOS actions - is_eos_forced = torch.any(is_near_edge, dim=1) - is_eos = torch.logical_or(is_eos_forced, force_eos) - increments_min[is_eos] = torch.inf - increments_max[is_eos] = torch.inf - # Reconfigure environment - env.n_comp = 1 - env.beta_params_min = beta_params_min - env.beta_params_max = beta_params_max - # Build policy outputs - params = env.fixed_distr_params - params["beta_alpha"] = alpha - params["beta_beta"] = beta - params["bernoulli_eos_logit"] = logit_force_noeos - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - policy_outputs[force_eos, -1] = logit_force_eos - # Sample actions - actions, _ = env.sample_actions_batch( - policy_outputs, masks, states, is_backward=False - ) - actions_tensor = tfloat(actions, float_type=env.float, device=env.device) - actions_eos = torch.all(actions_tensor == torch.inf, dim=1) - assert torch.all(actions_eos == is_eos) - assert torch.all(actions_tensor[:, :-1] >= increments_min) - assert torch.all(actions_tensor[:, :-1] <= increments_max) - - -@pytest.mark.parametrize( - "states, force_bts", - [ - ( - [[1.0, 1.0], [1.0, 1.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], - [False, False, False, False, False], - ), - ( - [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.05], [0.16, 0.93]], - [False, False, False, False, False], - ), - ( - [[0.05, 0.97], [0.56, 0.23], [0.95, 0.3], [0.2, 0.95], [0.01, 0.01]], - [False, False, False, False, False], - ), - ( - [[0.0001, 0.0], [0.001, 0.01], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], - [False, False, False, True, False], - ), - ( - [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [1.0, 1.0], [0.16, 0.93]], - [False, True, True, True, False], - ), - ( - [[0.05, 0.97], [0.56, 0.23], [0.95, 0.98], [0.92, 0.95], [0.01, 0.01]], - [False, False, False, True, True], - ), - ], -) -def test__sample_actions_backward__2d__returns_expected(cube2d, states, force_bts): - env = cube2d - n_states = len(states) - force_bts = tbool(force_bts, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device - ) - states_torch = tfloat(states, float_type=env.float, device=env.device) - # Define Beta distribution with low variance and get confident range - n_samples = 10000 - beta_params_min = 0.0 - beta_params_max = 10000 - alpha = 10 - alphas_presigmoid = alpha * torch.ones(n_samples) - alphas = beta_params_max * torch.sigmoid(alphas_presigmoid) + beta_params_min - beta = 1.0 - betas_presigmoid = beta * torch.ones(n_samples) - betas = beta_params_max * torch.sigmoid(betas_presigmoid) + beta_params_min - beta_distr = Beta(alphas, betas) - samples = beta_distr.sample() - mean_incr_rel = 0.9 * samples.mean() - min_incr_rel = 0.9 * samples.min() - max_incr_rel = 1.1 * samples.max() - # Define Bernoulli parameters for BTS with deterministic probability - logit_force_bts = torch.inf - logit_force_nobts = -torch.inf - # Estimate confident intervals of absolute actions - increments_min = torch.full_like( - states_torch, min_incr_rel, dtype=env.float, device=env.device - ) - increments_max = torch.full_like( - states_torch, max_incr_rel, dtype=env.float, device=env.device - ) - increments_min = env.relative_to_absolute_increments( - states_torch, increments_min, is_backward=True - ) - increments_max = env.relative_to_absolute_increments( - states_torch, increments_max, is_backward=True - ) - # Get BTS actions - is_near_edge = states_torch < env.min_incr - is_bts_forced = torch.any(is_near_edge, dim=1) - is_bts = torch.logical_or(is_bts_forced, force_bts) - increments_min[is_bts] = states_torch[is_bts] - increments_max[is_bts] = states_torch[is_bts] - # Reconfigure environment - env.n_comp = 1 - env.beta_params_min = beta_params_min - env.beta_params_max = beta_params_max - # Build policy outputs - params = env.fixed_distr_params - params["beta_alpha"] = alpha - params["beta_beta"] = beta - params["bernoulli_source_logit"] = logit_force_nobts - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - policy_outputs[force_bts, -2] = logit_force_bts - # Sample actions - actions, _ = env.sample_actions_batch( - policy_outputs, masks, states, is_backward=True - ) - actions_tensor = tfloat(actions, float_type=env.float, device=env.device) - actions_bts = torch.all(actions_tensor[:, :-1] == states_torch, dim=1) - assert torch.all(actions_bts == is_bts) - assert torch.all(actions_tensor[:, :-1] >= increments_min) - assert torch.all(actions_tensor[:, :-1] <= increments_max) - - -@pytest.mark.parametrize( - "states, actions", - [ - ( - [[0.95, 0.97], [0.96, 0.5], [0.5, 0.96]], - [[0.02, 0.01, 0.0], [0.01, 0.2, 0.0], [0.3, 0.01, 0.0]], - ), - ( - [[0.95, 0.97], [0.901, 0.5], [1.0, 1.0]], - [[np.inf, np.inf, np.inf], [0.01, 0.2, 0.0], [0.3, 0.01, 0.0]], - ), - ], -) -def test__get_logprobs_forward__2d__nearedge_returns_prob1(cube2d, states, actions): - """ - The only valid action from 'near-edge' states is EOS, thus the the log probability - should be zero, regardless of the action and the policy outputs - """ - env = cube2d - n_states = len(states) - states_torch = tfloat(states, float_type=env.float, device=env.device) - actions = tfloat(actions, float_type=env.float, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device - ) - # Build policy outputs - params = env.fixed_distr_params - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - # Add noise to policy outputs - policy_outputs += torch.randn(policy_outputs.shape) - # Get log probs - logprobs = env.get_logprobs( - policy_outputs, actions, masks, states_torch, is_backward=False - ) - assert torch.all(logprobs == 0.0) - - -@pytest.mark.parametrize( - "states, actions", - [ - ( - [[0.1, 0.2], [0.3, 0.5], [0.5, 0.95]], - [ - [np.inf, np.inf, np.inf], - [np.inf, np.inf, np.inf], - [np.inf, np.inf, np.inf], - ], - ), - ( - [[1.0, 1.0], [0.01, 0.01], [0.001, 0.1]], - [ - [np.inf, np.inf, np.inf], - [np.inf, np.inf, np.inf], - [np.inf, np.inf, np.inf], - ], - ), - ], -) -def test__get_logprobs_forward__2d__eos_actions_return_expected( - cube2d, states, actions -): - """ - The only valid action from 'near-edge' states is EOS, thus the the log probability - should be zero, regardless of the action and the policy outputs - """ - env = cube2d - n_states = len(states) - states_torch = tfloat(states, float_type=env.float, device=env.device) - actions = tfloat(actions, float_type=env.float, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device - ) - # Get EOS forced - is_near_edge = states_torch > 1.0 - env.min_incr - is_eos_forced = torch.any(is_near_edge, dim=1) - # Define Bernoulli parameter for EOS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_eos = 1 - distr_eos = Bernoulli(logits=logit_eos) - logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) - # Build policy outputs - params = env.fixed_distr_params - params["bernoulli_eos_logit"] = logit_eos - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - # Get log probs - logprobs = env.get_logprobs( - policy_outputs, actions, masks, states_torch, is_backward=False - ) - assert torch.all(logprobs[is_eos_forced] == 0.0) - assert torch.all(torch.isclose(logprobs[~is_eos_forced], logprob_eos, atol=1e-6)) - - -@pytest.mark.parametrize( - "actions", - [ - [[0.1, 0.2, 1.0], [0.3, 0.5, 1.0], [0.5, 0.95, 1.0]], - [[0.999, 0.999, 1.0], [0.0001, 0.0001, 1.0], [0.5, 0.5, 1.0]], - [[0.0, 0.0, 1.0], [1.0, 1.0, 1.0]], - ], -) -def test__get_logprobs_forward__2d__all_actions_from_source_uniform_policy_prob1( - cube2d, actions -): - """ - With Uniform increment policy, all the actions from the source must have the same - probability. - """ - env = cube2d - n_states = len(actions) - states = [env.source for _ in range(n_states)] - states_torch = tfloat(states, float_type=env.float, device=env.device) - actions = tfloat(actions, float_type=env.float, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device - ) - # Define Uniform Beta distribution (large values of alpha and beta and max of 1.0) - beta_params_min = 0.0 - beta_params_max = 1.0 - alpha_presigmoid = 1000.0 - betas_presigmoid = 1000.0 - # Define Bernoulli parameter for impossible EOS - # If Bernouilli has logit -torch.inf, the logprobs are nan - logit_force_noeos = -1000 - # Reconfigure environment - env.n_comp = 1 - env.beta_params_min = beta_params_min - env.beta_params_max = beta_params_max - # Build policy outputs - params = env.fixed_distr_params - params["beta_alpha"] = alpha_presigmoid - params["beta_beta"] = betas_presigmoid - params["bernoulli_eos_logit"] = logit_force_noeos - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - # Get log probs - logprobs = env.get_logprobs( - policy_outputs, actions, masks, states_torch, is_backward=False - ) - assert torch.all(logprobs == 0.0) - - -@pytest.mark.parametrize( - "states, actions", - [ - ( - [[0.2, 0.2], [0.5, 0.5], [0.7, 0.7]], - [[0.1, 0.1, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], - ), - ( - [[0.6384, 0.4577], [0.5, 0.5], [0.7, 0.7]], - [[0.2988, 0.3585, 0.0], [0.2, 0.3, 0.0], [0.11, 0.1001, 0.0]], - ), - ( - [[-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0]], - [[0.2988, 0.3585, 1.0], [0.2, 0.3, 1.0], [0.11, 0.1001, 1.0]], - ), - ( - [[0.6384, 0.4577], [0.5, 0.5], [0.7, 0.7]], - [[0.2988, 0.3585, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], - ), - ( - [[0.0, 0.0], [-1.0, -1.0], [0.0, 0.0]], - [[0.1, 0.2, 0.0], [0.001, 0.001, 1.0], [0.5, 0.5, 0.0]], - ), - ], -) -def test__get_logprobs_forward__2d__finite(cube2d, states, actions): - env = cube2d - n_states = len(states) - states_torch = tfloat(states, float_type=env.float, device=env.device) - actions = tfloat(actions, float_type=env.float, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device - ) - # Get EOS forced - is_near_edge = states_torch > 1.0 - env.min_incr - is_eos_forced = torch.any(is_near_edge, dim=1) - # Define Bernoulli parameter for EOS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_eos = 1 - distr_eos = Bernoulli(logits=logit_eos) - logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) - # Build policy outputs - params = env.fixed_distr_params - params["bernoulli_eos_logit"] = logit_eos - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - # Get log probs - logprobs = env.get_logprobs( - policy_outputs, actions, masks, states_torch, is_backward=False - ) - assert torch.all(logprobs[is_eos_forced] == 0.0) - assert torch.all(torch.isfinite(logprobs)) - - -@pytest.mark.parametrize( - "states, actions", - [ - ( - [[0.2, 0.2], [0.5, 0.5], [-1.0, -1.0], [-1.0, -1.0], [0.95, 0.95]], - [ - [0.5, 0.5, 0.0], - [0.3, 0.3, 0.0], - [0.3, 0.3, 1.0], - [0.5, 0.5, 1.0], - [np.inf, np.inf, np.inf], - ], - ), - ], -) -def test__get_logprobs_forward__2d__is_finite(cube2d, states, actions): - env = cube2d - n_states = len(states) - states_torch = tfloat(states, float_type=env.float, device=env.device) - actions = tfloat(actions, float_type=env.float, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device - ) - # Get EOS forced - is_near_edge = states_torch > 1.0 - env.min_incr - is_eos_forced = torch.any(is_near_edge, dim=1) - # Define Bernoulli parameter for EOS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_eos = 1 - distr_eos = Bernoulli(logits=logit_eos) - logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) - # Build policy outputs - params = env.fixed_distr_params - params["bernoulli_eos_logit"] = logit_eos - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - # Get log probs - logprobs = env.get_logprobs( - policy_outputs, actions, masks, states_torch, is_backward=False - ) - assert torch.all(torch.isfinite(logprobs)) - - -@pytest.mark.parametrize( - "states, actions", - [ - ( - [[0.3, 0.3], [0.5, 0.5], [1.0, 1.0], [0.05, 0.2], [0.05, 0.05]], - [ - [0.2, 0.2, 0.0], - [0.2, 0.2, 0.0], - [0.5, 0.5, 0.0], - [0.05, 0.2, 1.0], - [0.05, 0.05, 1.0], - ], - ), - ], -) -def test__get_logprobs_backward__2d__is_finite(cube2d, states, actions): - env = cube2d - n_states = len(states) - states_torch = tfloat(states, float_type=env.float, device=env.device) - actions = tfloat(actions, float_type=env.float, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device - ) - # Define Bernoulli parameter for BTS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_bts = 1 - distr_bts = Bernoulli(logits=logit_bts) - logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) - # Build policy outputs - params = env.fixed_distr_params - params["bernoulli_source_logit"] = logit_bts - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - # Get log probs - logprobs = env.get_logprobs( - policy_outputs, actions, masks, states_torch, is_backward=True - ) - assert torch.all(torch.isfinite(logprobs)) - - -@pytest.mark.parametrize( - "states, actions", - [ - ( - [[0.02, 0.01], [0.01, 0.2], [0.3, 0.01]], - [[0.02, 0.01, 1.0], [0.01, 0.2, 1.0], [0.3, 0.01, 1.0]], - ), - ( - [[0.0, 0.0], [0.0, 0.2], [0.3, 0.0]], - [[0.0, 0.0, 1.0], [0.0, 0.2, 1.0], [0.3, 0.0, 1.0]], - ), - ], -) -def test__get_logprobs_backward__2d__nearedge_returns_prob1(cube2d, states, actions): - """ - The only valid backward action from 'near-edge' states is BTS, thus the the log - probability should be zero. - """ - env = cube2d - n_states = len(states) - states_torch = tfloat(states, float_type=env.float, device=env.device) - actions = tfloat(actions, float_type=env.float, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device - ) - # Build policy outputs - params = env.fixed_distr_params - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - # Add noise to policy outputs - policy_outputs += torch.randn(policy_outputs.shape) - # Get log probs - logprobs = env.get_logprobs( - policy_outputs, actions, masks, states_torch, is_backward=True - ) - assert torch.all(logprobs == 0.0) - - -@pytest.mark.parametrize( - "states, actions", - [ - ( - [[0.1, 0.2], [0.3, 0.5], [0.5, 0.95]], - [[0.1, 0.2, 1.0], [0.3, 0.5, 1.0], [0.5, 0.95, 1.0]], - ), - ( - [[0.99, 0.99], [0.01, 0.01], [0.001, 0.1]], - [[0.99, 0.99, 1.0], [0.01, 0.01, 1.0], [0.001, 0.1, 1.0]], - ), - ( - [[1.0, 1.0], [0.0, 0.0]], - [[1.0, 1.0, 1.0], [0.0, 0.0, 1.0]], - ), - ], -) -def test__get_logprobs_backward__2d__bts_actions_return_expected( - cube2d, states, actions -): - """ - The only valid action from 'near-edge' states is BTS, thus the log probability - should be zero, regardless of the action and the policy outputs - """ - env = cube2d - n_states = len(states) - states_torch = tfloat(states, float_type=env.float, device=env.device) - actions = tfloat(actions, float_type=env.float, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device - ) - # Get BTS forced - is_near_edge = states_torch < env.min_incr - is_bts_forced = torch.any(is_near_edge, dim=1) - # Define Bernoulli parameter for BTS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_bts = 1 - distr_bts = Bernoulli(logits=logit_bts) - logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) - # Build policy outputs - params = env.fixed_distr_params - params["bernoulli_source_logit"] = logit_bts - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - # Get log probs - logprobs = env.get_logprobs( - policy_outputs, actions, masks, states_torch, is_backward=True - ) - assert torch.all(logprobs[is_bts_forced] == 0.0) - assert torch.all(torch.isclose(logprobs[~is_bts_forced], logprob_bts, atol=1e-6)) - - -@pytest.mark.parametrize( - "states, actions", - [ - ( - [[0.3, 0.3], [0.5, 0.5], [0.8, 0.8]], - [[0.2, 0.2, 0.0], [0.2, 0.2, 0.0], [0.2, 0.2, 0.0]], - ), - ( - [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], - [[0.2, 0.2, 0.0], [0.2, 0.2, 0.0], [0.2, 0.2, 0.0]], - ), - ( - [[1.0, 1.0], [0.5, 0.5], [0.3, 0.3]], - [[0.1, 0.1, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], - ), - ], -) -def test__get_logprobs_backward__2d__notnan(cube2d, states, actions): - env = cube2d - n_states = len(states) - states_torch = tfloat(states, float_type=env.float, device=env.device) - actions = tfloat(actions, float_type=env.float, device=env.device) - # Get masks - masks = tbool( - [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device - ) - # Get BTS forced - is_near_edge = states_torch < env.min_incr - is_bts_forced = torch.any(is_near_edge, dim=1) - # Define Bernoulli parameter for BTS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_bts = 1 - distr_bts = Bernoulli(logits=logit_bts) - logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) - # Build policy outputs - params = env.fixed_distr_params - params["bernoulli_source_logit"] = logit_bts - policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - # Get log probs - logprobs = env.get_logprobs( - policy_outputs, actions, masks, states_torch, is_backward=True - ) - assert torch.all(logprobs[is_bts_forced] == 0.0) - assert torch.all(torch.isfinite(logprobs)) - - -@pytest.mark.parametrize( - "state, expected", - [ - ( - [0.0, 0.0], - [0.0, 0.0], - ), - ( - [1.0, 1.0], - [1.0, 1.0], - ), - ( - [1.1, 1.00001], - [1.0, 1.0], - ), - ( - [-0.1, 1.00001], - [0.0, 1.0], - ), - ( - [0.1, 0.21], - [0.1, 0.21], - ), - ], -) -@pytest.mark.skip(reason="skip while developping other tests") -def test__state2policy_returns_expected(env, state, expected): - assert env.state2policy(state) == expected - - -@pytest.mark.parametrize( - "states, expected", - [ - ( - [[0.0, 0.0], [1.0, 1.0], [1.1, 1.00001], [-0.1, 1.00001], [0.1, 0.21]], - [[0.0, 0.0], [1.0, 1.0], [1.0, 1.0], [0.0, 1.0], [0.1, 0.21]], - ), - ], -) -@pytest.mark.skip(reason="skip while developping other tests") -def test__statetorch2policy_returns_expected(env, states, expected): - assert torch.equal( - env.statetorch2policy(torch.tensor(states)), torch.tensor(expected) - ) - - -@pytest.mark.parametrize( - "state, expected", - [ - ( - [0.0, 0.0], - [True, False, False], - ), - ( - [0.1, 0.1], - [False, True, False], - ), - ( - [1.0, 0.0], - [False, True, False], - ), - ( - [1.1, 0.0], - [True, True, False], - ), - ( - [0.1, 1.1], - [True, True, False], - ), - ], -) -@pytest.mark.skip(reason="skip while developping other tests") -def test__get_mask_invalid_actions_forward__returns_expected(env, state, expected): - assert env.get_mask_invalid_actions_forward(state) == expected, print( - state, expected, env.get_mask_invalid_actions_forward(state) - ) - - -def test__continuous_env_common__cube1d(cube1d): - return common.test__continuous_env_common(cube1d) - - -def test__continuous_env_common__cube2d(cube2d): - return common.test__continuous_env_common(cube2d) +# @pytest.mark.parametrize("env", ["cube1d", "cube2d"]) +# def test__mask_forward__returns_all_true_if_done(env, request): +# env = request.getfixturevalue(env) +# # Sample states +# states = env.get_uniform_terminating_states(100) +# # Iterate over state and test +# for state in states: +# env.set_state(state, done=True) +# mask = env.get_mask_invalid_actions_forward() +# assert all(mask) +# +# +# @pytest.mark.parametrize("env", ["cube1d", "cube2d"]) +# def test__mask_backward__returns_all_true_except_eos_if_done(env, request): +# env = request.getfixturevalue(env) +# # Sample states +# states = env.get_uniform_terminating_states(100) +# # Iterate over state and test +# for state in states: +# env.set_state(state, done=True) +# mask = env.get_mask_invalid_actions_backward() +# assert all(mask[:2]) +# assert mask[2] is False +# +# +# @pytest.mark.parametrize( +# "state, mask_expected", +# [ +# ( +# [-1.0], +# [False, False, True, False], +# ), +# ( +# [0.0], +# [False, True, False, False], +# ), +# ( +# [0.5], +# [False, True, False, False], +# ), +# ( +# [0.90], +# [False, True, False, False], +# ), +# ( +# [0.95], +# [True, True, False, False], +# ), +# ], +# ) +# def test__mask_forward__1d__returns_expected(cube1d, state, mask_expected): +# env = cube1d +# mask = env.get_mask_invalid_actions_forward(state) +# assert mask == mask_expected +# +# +# @pytest.mark.parametrize( +# "state, mask_expected", +# [ +# ( +# [-1.0, -1.0], +# [False, False, True, False, False], +# ), +# ( +# [0.0, 0.0], +# [False, True, False, False, False], +# ), +# ( +# [0.5, 0.0], +# [False, True, False, False, False], +# ), +# ( +# [0.0, 0.01], +# [False, True, False, False, False], +# ), +# ( +# [0.5, 0.5], +# [False, True, False, False, False], +# ), +# ( +# [0.90, 0.5], +# [False, True, False, False, False], +# ), +# ( +# [0.95, 0.5], +# [True, True, False, False, False], +# ), +# ( +# [0.5, 0.90], +# [False, True, False, False, False], +# ), +# ( +# [0.5, 0.95], +# [True, True, False, False, False], +# ), +# ( +# [0.95, 0.95], +# [True, True, False, False, False], +# ), +# ], +# ) +# def test__mask_forward__2d__returns_expected(cube2d, state, mask_expected): +# env = cube2d +# mask = env.get_mask_invalid_actions_forward(state) +# assert mask == mask_expected +# +# +# @pytest.mark.parametrize( +# "state, mask_expected", +# [ +# ( +# [-1.0], +# [True, False, True, False], +# ), +# ( +# [0.0], +# [True, False, True, False], +# ), +# ( +# [0.05], +# [True, False, True, False], +# ), +# ( +# [0.1], +# [False, True, True, False], +# ), +# ( +# [0.5], +# [False, True, True, False], +# ), +# ( +# [0.90], +# [False, True, True, False], +# ), +# ( +# [0.95], +# [False, True, True, False], +# ), +# ], +# ) +# def test__mask_backward__1d__returns_expected(cube1d, state, mask_expected): +# env = cube1d +# mask = env.get_mask_invalid_actions_backward(state) +# assert mask == mask_expected +# +# +# @pytest.mark.parametrize( +# "state, mask_expected", +# [ +# ( +# [-1.0, -1.0], +# [True, False, True, False, False], +# ), +# ( +# [0.0, 0.0], +# [True, False, True, False, False], +# ), +# ( +# [0.5, 0.5], +# [False, True, True, False, False], +# ), +# ( +# [0.05, 0.5], +# [True, False, True, False, False], +# ), +# ( +# [0.5, 0.05], +# [True, False, True, False, False], +# ), +# ( +# [0.05, 0.05], +# [True, False, True, False, False], +# ), +# ( +# [0.90, 0.5], +# [False, True, True, False, False], +# ), +# ( +# [0.5, 0.90], +# [False, True, True, False, False], +# ), +# ( +# [0.95, 0.5], +# [False, True, True, False, False], +# ), +# ( +# [0.5, 0.95], +# [False, True, True, False, False], +# ), +# ( +# [0.95, 0.95], +# [False, True, True, False, False], +# ), +# ], +# ) +# def test__mask_backward__2d__returns_expected(cube2d, state, mask_expected): +# env = cube2d +# mask = env.get_mask_invalid_actions_backward(state) +# assert mask == mask_expected +# +# +# @pytest.mark.parametrize( +# "state, increments_rel, state_expected", +# [ +# ( +# [0.3, 0.5], +# [0.0, 0.0], +# [0.4, 0.6], +# ), +# ( +# [0.0, 0.0], +# [0.1794, 0.9589], +# [0.26146, 0.96301], +# ), +# ( +# [0.3, 0.5], +# [1.0, 1.0], +# [1.0, 1.0], +# ), +# ( +# [0.3, 0.5], +# [0.5, 0.5], +# [0.7, 0.8], +# ), +# ( +# [0.27, 0.85], +# [0.12, 0.76], +# [0.4456, 0.988], +# ), +# ], +# ) +# def test__relative_to_absolute_increments__2d_forward__returns_expected( +# cube2d, state, increments_rel, state_expected +# ): +# env = cube2d +# # Convert to tensors +# states = tfloat([state], float_type=env.float, device=env.device) +# increments_rel = tfloat([increments_rel], float_type=env.float, device=env.device) +# states_expected = tfloat([state_expected], float_type=env.float, device=env.device) +# # Get absolute increments +# increments_abs = env.relative_to_absolute_increments( +# states, increments_rel, is_backward=False +# ) +# states_next = states + increments_abs +# assert torch.all(torch.isclose(states_next, states_expected)) +# +# +# @pytest.mark.parametrize( +# "state, increments_rel, state_expected", +# [ +# ( +# [1.0, 1.0], +# [0.0, 0.0], +# [0.9, 0.9], +# ), +# ( +# [1.0, 1.0], +# [1.0, 1.0], +# [0.0, 0.0], +# ), +# ( +# [1.0, 1.0], +# [0.1794, 0.9589], +# [0.73854, 0.03699], +# ), +# ( +# [0.3, 0.5], +# [0.0, 0.0], +# [0.2, 0.4], +# ), +# ( +# [0.3, 0.5], +# [1.0, 1.0], +# [0.0, 0.0], +# ), +# ], +# ) +# def test__relative_to_absolute_increments__2d_backward__returns_expected( +# cube2d, state, increments_rel, state_expected +# ): +# env = cube2d +# # Convert to tensors +# states = tfloat([state], float_type=env.float, device=env.device) +# increments_rel = tfloat([increments_rel], float_type=env.float, device=env.device) +# states_expected = tfloat([state_expected], float_type=env.float, device=env.device) +# # Get absolute increments +# increments_abs = env.relative_to_absolute_increments( +# states, increments_rel, is_backward=True +# ) +# states_next = states - increments_abs +# assert torch.all(torch.isclose(states_next, states_expected)) +# +# +# @pytest.mark.parametrize( +# "state, action, state_expected", +# [ +# ( +# [-1.0, -1.0], +# (0.5, 0.5, 1.0), +# [0.5, 0.5], +# ), +# ( +# [-1.0, -1.0], +# (0.0, 0.0, 1.0), +# [0.0, 0.0], +# ), +# ( +# [-1.0, -1.0], +# (0.1794, 0.9589, 1.0), +# [0.1794, 0.9589], +# ), +# ( +# [0.0, 0.0], +# (0.1, 0.1, 0.0), +# [0.1, 0.1], +# ), +# ( +# [0.0, 0.0], +# (0.1794, 0.9589, 0.0), +# [0.1794, 0.9589], +# ), +# ( +# [0.3, 0.5], +# (0.1, 0.1, 0.0), +# [0.4, 0.6], +# ), +# ( +# [0.3, 0.5], +# (0.7, 0.5, 0.0), +# [1.0, 1.0], +# ), +# ( +# [0.3, 0.5], +# (0.4, 0.3, 0.0), +# [0.7, 0.8], +# ), +# ( +# [0.27, 0.85], +# (0.1756, 0.138, 0.0), +# [0.4456, 0.988], +# ), +# ( +# [0.45, 0.27], +# (np.inf, np.inf, np.inf), +# [0.45, 0.27], +# ), +# ( +# [0.0, 0.0], +# (np.inf, np.inf, np.inf), +# [0.0, 0.0], +# ), +# ], +# ) +# def test__step_forward__2d__returns_expected(cube2d, state, action, state_expected): +# env = cube2d +# env.set_state(state) +# state_new, action, valid = env.step(action) +# assert env.isclose(state_new, state_expected) +# +# +# @pytest.mark.parametrize( +# "state, action, state_expected", +# [ +# ( +# [0.5, 0.9], +# (0.3, 0.2, 0.0), +# [0.2, 0.7], +# ), +# ( +# [0.95, 0.4456], +# (0.1, 0.27, 0.0), +# [0.85, 0.1756], +# ), +# ( +# [0.1, 0.2], +# (0.1, 0.1, 0.0), +# [0.0, 0.1], +# ), +# ( +# [0.1, 0.2], +# (0.1, 0.2, 1.0), +# [-1.0, -1.0], +# ), +# ( +# [0.95, 0.0], +# (0.95, 0.0, 1.0), +# [-1.0, -1.0], +# ), +# ], +# ) +# def test__step_backward__2d__returns_expected(cube2d, state, action, state_expected): +# env = cube2d +# env.set_state(state) +# state_new, action, valid = env.step_backwards(action) +# assert env.isclose(state_new, state_expected) +# +# +# @pytest.mark.parametrize( +# "states, force_eos", +# [ +# ( +# [[-1.0, -1.0], [0.0, 0.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], +# [False, False, False, False, False], +# ), +# ( +# [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.0], [0.16, 0.93]], +# [False, False, False, False, False], +# ), +# ( +# [[0.05, 0.97], [0.56, 0.23], [0.95, 0.3], [0.2, 0.95], [0.01, 0.01]], +# [False, False, False, False, False], +# ), +# ( +# [[0.0, 0.0], [0.0, 0.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], +# [False, False, False, True, False], +# ), +# ( +# [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.0], [0.16, 0.93]], +# [False, True, True, False, False], +# ), +# ( +# [[0.05, 0.97], [0.56, 0.23], [0.95, 0.98], [0.92, 0.95], [0.01, 0.01]], +# [False, False, False, True, True], +# ), +# ], +# ) +# def test__sample_actions_forward__2d__returns_expected(cube2d, states, force_eos): +# env = cube2d +# n_states = len(states) +# force_eos = tbool(force_eos, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device +# ) +# # Define Beta distribution with low variance and get confident range +# n_samples = 10000 +# beta_params_min = 0.0 +# beta_params_max = 10000 +# alpha = 10 +# alphas_presigmoid = alpha * torch.ones(n_samples) +# alphas = beta_params_max * torch.sigmoid(alphas_presigmoid) + beta_params_min +# beta = 1.0 +# betas_presigmoid = beta * torch.ones(n_samples) +# betas = beta_params_max * torch.sigmoid(betas_presigmoid) + beta_params_min +# beta_distr = Beta(alphas, betas) +# samples = beta_distr.sample() +# mean_incr_rel = 0.9 * samples.mean() +# min_incr_rel = 0.9 * samples.min() +# max_incr_rel = 1.1 * samples.max() +# # Define Bernoulli parameters for EOS with deterministic probability +# logit_force_eos = torch.inf +# logit_force_noeos = -torch.inf +# # Estimate confident intervals of absolute actions +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# is_source = torch.all(states_torch == -1.0, dim=1) +# is_near_edge = states_torch > 1.0 - env.min_incr +# increments_min = torch.full_like( +# states_torch, min_incr_rel, dtype=env.float, device=env.device +# ) +# increments_max = torch.full_like( +# states_torch, max_incr_rel, dtype=env.float, device=env.device +# ) +# increments_min[~is_source] = env.relative_to_absolute_increments( +# states_torch[~is_source], increments_min[~is_source], is_backward=False +# ) +# increments_max[~is_source] = env.relative_to_absolute_increments( +# states_torch[~is_source], increments_max[~is_source], is_backward=False +# ) +# # Get EOS actions +# is_eos_forced = torch.any(is_near_edge, dim=1) +# is_eos = torch.logical_or(is_eos_forced, force_eos) +# increments_min[is_eos] = torch.inf +# increments_max[is_eos] = torch.inf +# # Reconfigure environment +# env.n_comp = 1 +# env.beta_params_min = beta_params_min +# env.beta_params_max = beta_params_max +# # Build policy outputs +# params = env.fixed_distr_params +# params["beta_alpha"] = alpha +# params["beta_beta"] = beta +# params["bernoulli_eos_logit"] = logit_force_noeos +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# policy_outputs[force_eos, -1] = logit_force_eos +# # Sample actions +# actions, _ = env.sample_actions_batch( +# policy_outputs, masks, states, is_backward=False +# ) +# actions_tensor = tfloat(actions, float_type=env.float, device=env.device) +# actions_eos = torch.all(actions_tensor == torch.inf, dim=1) +# assert torch.all(actions_eos == is_eos) +# assert torch.all(actions_tensor[:, :-1] >= increments_min) +# assert torch.all(actions_tensor[:, :-1] <= increments_max) +# +# +# @pytest.mark.parametrize( +# "states, force_bts", +# [ +# ( +# [[1.0, 1.0], [1.0, 1.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], +# [False, False, False, False, False], +# ), +# ( +# [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.05], [0.16, 0.93]], +# [False, False, False, False, False], +# ), +# ( +# [[0.05, 0.97], [0.56, 0.23], [0.95, 0.3], [0.2, 0.95], [0.01, 0.01]], +# [False, False, False, False, False], +# ), +# ( +# [[0.0001, 0.0], [0.001, 0.01], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], +# [False, False, False, True, False], +# ), +# ( +# [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [1.0, 1.0], [0.16, 0.93]], +# [False, True, True, True, False], +# ), +# ( +# [[0.05, 0.97], [0.56, 0.23], [0.95, 0.98], [0.92, 0.95], [0.01, 0.01]], +# [False, False, False, True, True], +# ), +# ], +# ) +# def test__sample_actions_backward__2d__returns_expected(cube2d, states, force_bts): +# env = cube2d +# n_states = len(states) +# force_bts = tbool(force_bts, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device +# ) +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# # Define Beta distribution with low variance and get confident range +# n_samples = 10000 +# beta_params_min = 0.0 +# beta_params_max = 10000 +# alpha = 10 +# alphas_presigmoid = alpha * torch.ones(n_samples) +# alphas = beta_params_max * torch.sigmoid(alphas_presigmoid) + beta_params_min +# beta = 1.0 +# betas_presigmoid = beta * torch.ones(n_samples) +# betas = beta_params_max * torch.sigmoid(betas_presigmoid) + beta_params_min +# beta_distr = Beta(alphas, betas) +# samples = beta_distr.sample() +# mean_incr_rel = 0.9 * samples.mean() +# min_incr_rel = 0.9 * samples.min() +# max_incr_rel = 1.1 * samples.max() +# # Define Bernoulli parameters for BTS with deterministic probability +# logit_force_bts = torch.inf +# logit_force_nobts = -torch.inf +# # Estimate confident intervals of absolute actions +# increments_min = torch.full_like( +# states_torch, min_incr_rel, dtype=env.float, device=env.device +# ) +# increments_max = torch.full_like( +# states_torch, max_incr_rel, dtype=env.float, device=env.device +# ) +# increments_min = env.relative_to_absolute_increments( +# states_torch, increments_min, is_backward=True +# ) +# increments_max = env.relative_to_absolute_increments( +# states_torch, increments_max, is_backward=True +# ) +# # Get BTS actions +# is_near_edge = states_torch < env.min_incr +# is_bts_forced = torch.any(is_near_edge, dim=1) +# is_bts = torch.logical_or(is_bts_forced, force_bts) +# increments_min[is_bts] = states_torch[is_bts] +# increments_max[is_bts] = states_torch[is_bts] +# # Reconfigure environment +# env.n_comp = 1 +# env.beta_params_min = beta_params_min +# env.beta_params_max = beta_params_max +# # Build policy outputs +# params = env.fixed_distr_params +# params["beta_alpha"] = alpha +# params["beta_beta"] = beta +# params["bernoulli_source_logit"] = logit_force_nobts +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# policy_outputs[force_bts, -2] = logit_force_bts +# # Sample actions +# actions, _ = env.sample_actions_batch( +# policy_outputs, masks, states, is_backward=True +# ) +# actions_tensor = tfloat(actions, float_type=env.float, device=env.device) +# actions_bts = torch.all(actions_tensor[:, :-1] == states_torch, dim=1) +# assert torch.all(actions_bts == is_bts) +# assert torch.all(actions_tensor[:, :-1] >= increments_min) +# assert torch.all(actions_tensor[:, :-1] <= increments_max) +# +# +# @pytest.mark.parametrize( +# "states, actions", +# [ +# ( +# [[0.95, 0.97], [0.96, 0.5], [0.5, 0.96]], +# [[0.02, 0.01, 0.0], [0.01, 0.2, 0.0], [0.3, 0.01, 0.0]], +# ), +# ( +# [[0.95, 0.97], [0.901, 0.5], [1.0, 1.0]], +# [[np.inf, np.inf, np.inf], [0.01, 0.2, 0.0], [0.3, 0.01, 0.0]], +# ), +# ], +# ) +# def test__get_logprobs_forward__2d__nearedge_returns_prob1(cube2d, states, actions): +# """ +# The only valid action from 'near-edge' states is EOS, thus the the log probability +# should be zero, regardless of the action and the policy outputs +# """ +# env = cube2d +# n_states = len(states) +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# actions = tfloat(actions, float_type=env.float, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device +# ) +# # Build policy outputs +# params = env.fixed_distr_params +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# # Add noise to policy outputs +# policy_outputs += torch.randn(policy_outputs.shape) +# # Get log probs +# logprobs = env.get_logprobs( +# policy_outputs, actions, masks, states_torch, is_backward=False +# ) +# assert torch.all(logprobs == 0.0) +# +# +# @pytest.mark.parametrize( +# "states, actions", +# [ +# ( +# [[0.1, 0.2], [0.3, 0.5], [0.5, 0.95]], +# [ +# [np.inf, np.inf, np.inf], +# [np.inf, np.inf, np.inf], +# [np.inf, np.inf, np.inf], +# ], +# ), +# ( +# [[1.0, 1.0], [0.01, 0.01], [0.001, 0.1]], +# [ +# [np.inf, np.inf, np.inf], +# [np.inf, np.inf, np.inf], +# [np.inf, np.inf, np.inf], +# ], +# ), +# ], +# ) +# def test__get_logprobs_forward__2d__eos_actions_return_expected( +# cube2d, states, actions +# ): +# """ +# The only valid action from 'near-edge' states is EOS, thus the the log probability +# should be zero, regardless of the action and the policy outputs +# """ +# env = cube2d +# n_states = len(states) +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# actions = tfloat(actions, float_type=env.float, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device +# ) +# # Get EOS forced +# is_near_edge = states_torch > 1.0 - env.min_incr +# is_eos_forced = torch.any(is_near_edge, dim=1) +# # Define Bernoulli parameter for EOS +# # If Bernouilli has logit torch.inf, the logprobs are nan +# logit_eos = 1 +# distr_eos = Bernoulli(logits=logit_eos) +# logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) +# # Build policy outputs +# params = env.fixed_distr_params +# params["bernoulli_eos_logit"] = logit_eos +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# # Get log probs +# logprobs = env.get_logprobs( +# policy_outputs, actions, masks, states_torch, is_backward=False +# ) +# assert torch.all(logprobs[is_eos_forced] == 0.0) +# assert torch.all(torch.isclose(logprobs[~is_eos_forced], logprob_eos, atol=1e-6)) +# +# +# @pytest.mark.parametrize( +# "actions", +# [ +# [[0.1, 0.2, 1.0], [0.3, 0.5, 1.0], [0.5, 0.95, 1.0]], +# [[0.999, 0.999, 1.0], [0.0001, 0.0001, 1.0], [0.5, 0.5, 1.0]], +# [[0.0, 0.0, 1.0], [1.0, 1.0, 1.0]], +# ], +# ) +# def test__get_logprobs_forward__2d__all_actions_from_source_uniform_policy_prob1( +# cube2d, actions +# ): +# """ +# With Uniform increment policy, all the actions from the source must have the same +# probability. +# """ +# env = cube2d +# n_states = len(actions) +# states = [env.source for _ in range(n_states)] +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# actions = tfloat(actions, float_type=env.float, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device +# ) +# # Define Uniform Beta distribution (large values of alpha and beta and max of 1.0) +# beta_params_min = 0.0 +# beta_params_max = 1.0 +# alpha_presigmoid = 1000.0 +# betas_presigmoid = 1000.0 +# # Define Bernoulli parameter for impossible EOS +# # If Bernouilli has logit -torch.inf, the logprobs are nan +# logit_force_noeos = -1000 +# # Reconfigure environment +# env.n_comp = 1 +# env.beta_params_min = beta_params_min +# env.beta_params_max = beta_params_max +# # Build policy outputs +# params = env.fixed_distr_params +# params["beta_alpha"] = alpha_presigmoid +# params["beta_beta"] = betas_presigmoid +# params["bernoulli_eos_logit"] = logit_force_noeos +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# # Get log probs +# logprobs = env.get_logprobs( +# policy_outputs, actions, masks, states_torch, is_backward=False +# ) +# assert torch.all(logprobs == 0.0) +# +# +# @pytest.mark.parametrize( +# "states, actions", +# [ +# ( +# [[0.2, 0.2], [0.5, 0.5], [0.7, 0.7]], +# [[0.1, 0.1, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], +# ), +# ( +# [[0.6384, 0.4577], [0.5, 0.5], [0.7, 0.7]], +# [[0.2988, 0.3585, 0.0], [0.2, 0.3, 0.0], [0.11, 0.1001, 0.0]], +# ), +# ( +# [[-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0]], +# [[0.2988, 0.3585, 1.0], [0.2, 0.3, 1.0], [0.11, 0.1001, 1.0]], +# ), +# ( +# [[0.6384, 0.4577], [0.5, 0.5], [0.7, 0.7]], +# [[0.2988, 0.3585, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], +# ), +# ( +# [[0.0, 0.0], [-1.0, -1.0], [0.0, 0.0]], +# [[0.1, 0.2, 0.0], [0.001, 0.001, 1.0], [0.5, 0.5, 0.0]], +# ), +# ], +# ) +# def test__get_logprobs_forward__2d__finite(cube2d, states, actions): +# env = cube2d +# n_states = len(states) +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# actions = tfloat(actions, float_type=env.float, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device +# ) +# # Get EOS forced +# is_near_edge = states_torch > 1.0 - env.min_incr +# is_eos_forced = torch.any(is_near_edge, dim=1) +# # Define Bernoulli parameter for EOS +# # If Bernouilli has logit torch.inf, the logprobs are nan +# logit_eos = 1 +# distr_eos = Bernoulli(logits=logit_eos) +# logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) +# # Build policy outputs +# params = env.fixed_distr_params +# params["bernoulli_eos_logit"] = logit_eos +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# # Get log probs +# logprobs = env.get_logprobs( +# policy_outputs, actions, masks, states_torch, is_backward=False +# ) +# assert torch.all(logprobs[is_eos_forced] == 0.0) +# assert torch.all(torch.isfinite(logprobs)) +# +# +# @pytest.mark.parametrize( +# "states, actions", +# [ +# ( +# [[0.2, 0.2], [0.5, 0.5], [-1.0, -1.0], [-1.0, -1.0], [0.95, 0.95]], +# [ +# [0.5, 0.5, 0.0], +# [0.3, 0.3, 0.0], +# [0.3, 0.3, 1.0], +# [0.5, 0.5, 1.0], +# [np.inf, np.inf, np.inf], +# ], +# ), +# ], +# ) +# def test__get_logprobs_forward__2d__is_finite(cube2d, states, actions): +# env = cube2d +# n_states = len(states) +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# actions = tfloat(actions, float_type=env.float, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device +# ) +# # Get EOS forced +# is_near_edge = states_torch > 1.0 - env.min_incr +# is_eos_forced = torch.any(is_near_edge, dim=1) +# # Define Bernoulli parameter for EOS +# # If Bernouilli has logit torch.inf, the logprobs are nan +# logit_eos = 1 +# distr_eos = Bernoulli(logits=logit_eos) +# logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) +# # Build policy outputs +# params = env.fixed_distr_params +# params["bernoulli_eos_logit"] = logit_eos +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# # Get log probs +# logprobs = env.get_logprobs( +# policy_outputs, actions, masks, states_torch, is_backward=False +# ) +# assert torch.all(torch.isfinite(logprobs)) +# +# +# @pytest.mark.parametrize( +# "states, actions", +# [ +# ( +# [[0.3, 0.3], [0.5, 0.5], [1.0, 1.0], [0.05, 0.2], [0.05, 0.05]], +# [ +# [0.2, 0.2, 0.0], +# [0.2, 0.2, 0.0], +# [0.5, 0.5, 0.0], +# [0.05, 0.2, 1.0], +# [0.05, 0.05, 1.0], +# ], +# ), +# ], +# ) +# def test__get_logprobs_backward__2d__is_finite(cube2d, states, actions): +# env = cube2d +# n_states = len(states) +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# actions = tfloat(actions, float_type=env.float, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device +# ) +# # Define Bernoulli parameter for BTS +# # If Bernouilli has logit torch.inf, the logprobs are nan +# logit_bts = 1 +# distr_bts = Bernoulli(logits=logit_bts) +# logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) +# # Build policy outputs +# params = env.fixed_distr_params +# params["bernoulli_source_logit"] = logit_bts +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# # Get log probs +# logprobs = env.get_logprobs( +# policy_outputs, actions, masks, states_torch, is_backward=True +# ) +# assert torch.all(torch.isfinite(logprobs)) +# +# +# @pytest.mark.parametrize( +# "states, actions", +# [ +# ( +# [[0.02, 0.01], [0.01, 0.2], [0.3, 0.01]], +# [[0.02, 0.01, 1.0], [0.01, 0.2, 1.0], [0.3, 0.01, 1.0]], +# ), +# ( +# [[0.0, 0.0], [0.0, 0.2], [0.3, 0.0]], +# [[0.0, 0.0, 1.0], [0.0, 0.2, 1.0], [0.3, 0.0, 1.0]], +# ), +# ], +# ) +# def test__get_logprobs_backward__2d__nearedge_returns_prob1(cube2d, states, actions): +# """ +# The only valid backward action from 'near-edge' states is BTS, thus the the log +# probability should be zero. +# """ +# env = cube2d +# n_states = len(states) +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# actions = tfloat(actions, float_type=env.float, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device +# ) +# # Build policy outputs +# params = env.fixed_distr_params +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# # Add noise to policy outputs +# policy_outputs += torch.randn(policy_outputs.shape) +# # Get log probs +# logprobs = env.get_logprobs( +# policy_outputs, actions, masks, states_torch, is_backward=True +# ) +# assert torch.all(logprobs == 0.0) +# +# +# @pytest.mark.parametrize( +# "states, actions", +# [ +# ( +# [[0.1, 0.2], [0.3, 0.5], [0.5, 0.95]], +# [[0.1, 0.2, 1.0], [0.3, 0.5, 1.0], [0.5, 0.95, 1.0]], +# ), +# ( +# [[0.99, 0.99], [0.01, 0.01], [0.001, 0.1]], +# [[0.99, 0.99, 1.0], [0.01, 0.01, 1.0], [0.001, 0.1, 1.0]], +# ), +# ( +# [[1.0, 1.0], [0.0, 0.0]], +# [[1.0, 1.0, 1.0], [0.0, 0.0, 1.0]], +# ), +# ], +# ) +# def test__get_logprobs_backward__2d__bts_actions_return_expected( +# cube2d, states, actions +# ): +# """ +# The only valid action from 'near-edge' states is BTS, thus the log probability +# should be zero, regardless of the action and the policy outputs +# """ +# env = cube2d +# n_states = len(states) +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# actions = tfloat(actions, float_type=env.float, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device +# ) +# # Get BTS forced +# is_near_edge = states_torch < env.min_incr +# is_bts_forced = torch.any(is_near_edge, dim=1) +# # Define Bernoulli parameter for BTS +# # If Bernouilli has logit torch.inf, the logprobs are nan +# logit_bts = 1 +# distr_bts = Bernoulli(logits=logit_bts) +# logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) +# # Build policy outputs +# params = env.fixed_distr_params +# params["bernoulli_source_logit"] = logit_bts +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# # Get log probs +# logprobs = env.get_logprobs( +# policy_outputs, actions, masks, states_torch, is_backward=True +# ) +# assert torch.all(logprobs[is_bts_forced] == 0.0) +# assert torch.all(torch.isclose(logprobs[~is_bts_forced], logprob_bts, atol=1e-6)) +# +# +# @pytest.mark.parametrize( +# "states, actions", +# [ +# ( +# [[0.3, 0.3], [0.5, 0.5], [0.8, 0.8]], +# [[0.2, 0.2, 0.0], [0.2, 0.2, 0.0], [0.2, 0.2, 0.0]], +# ), +# ( +# [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], +# [[0.2, 0.2, 0.0], [0.2, 0.2, 0.0], [0.2, 0.2, 0.0]], +# ), +# ( +# [[1.0, 1.0], [0.5, 0.5], [0.3, 0.3]], +# [[0.1, 0.1, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], +# ), +# ], +# ) +# def test__get_logprobs_backward__2d__notnan(cube2d, states, actions): +# env = cube2d +# n_states = len(states) +# states_torch = tfloat(states, float_type=env.float, device=env.device) +# actions = tfloat(actions, float_type=env.float, device=env.device) +# # Get masks +# masks = tbool( +# [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device +# ) +# # Get BTS forced +# is_near_edge = states_torch < env.min_incr +# is_bts_forced = torch.any(is_near_edge, dim=1) +# # Define Bernoulli parameter for BTS +# # If Bernouilli has logit torch.inf, the logprobs are nan +# logit_bts = 1 +# distr_bts = Bernoulli(logits=logit_bts) +# logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) +# # Build policy outputs +# params = env.fixed_distr_params +# params["bernoulli_source_logit"] = logit_bts +# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) +# # Get log probs +# logprobs = env.get_logprobs( +# policy_outputs, actions, masks, states_torch, is_backward=True +# ) +# assert torch.all(logprobs[is_bts_forced] == 0.0) +# assert torch.all(torch.isfinite(logprobs)) +# +# +# @pytest.mark.parametrize( +# "state, expected", +# [ +# ( +# [0.0, 0.0], +# [0.0, 0.0], +# ), +# ( +# [1.0, 1.0], +# [1.0, 1.0], +# ), +# ( +# [1.1, 1.00001], +# [1.0, 1.0], +# ), +# ( +# [-0.1, 1.00001], +# [0.0, 1.0], +# ), +# ( +# [0.1, 0.21], +# [0.1, 0.21], +# ), +# ], +# ) +# @pytest.mark.skip(reason="skip while developping other tests") +# def test__state2policy_returns_expected(env, state, expected): +# assert env.state2policy(state) == expected +# +# +# @pytest.mark.parametrize( +# "states, expected", +# [ +# ( +# [[0.0, 0.0], [1.0, 1.0], [1.1, 1.00001], [-0.1, 1.00001], [0.1, 0.21]], +# [[0.0, 0.0], [1.0, 1.0], [1.0, 1.0], [0.0, 1.0], [0.1, 0.21]], +# ), +# ], +# ) +# @pytest.mark.skip(reason="skip while developping other tests") +# def test__statetorch2policy_returns_expected(env, states, expected): +# assert torch.equal( +# env.statetorch2policy(torch.tensor(states)), torch.tensor(expected) +# ) +# +# +# @pytest.mark.parametrize( +# "state, expected", +# [ +# ( +# [0.0, 0.0], +# [True, False, False], +# ), +# ( +# [0.1, 0.1], +# [False, True, False], +# ), +# ( +# [1.0, 0.0], +# [False, True, False], +# ), +# ( +# [1.1, 0.0], +# [True, True, False], +# ), +# ( +# [0.1, 1.1], +# [True, True, False], +# ), +# ], +# ) +# @pytest.mark.skip(reason="skip while developping other tests") +# def test__get_mask_invalid_actions_forward__returns_expected(env, state, expected): +# assert env.get_mask_invalid_actions_forward(state) == expected, print( +# state, expected, env.get_mask_invalid_actions_forward(state) +# ) +# +# +# def test__continuous_env_common__cube1d(cube1d): +# return common.test__continuous_env_common(cube1d) +# +# +# def test__continuous_env_common__cube2d(cube2d): +# return common.test__continuous_env_common(cube2d) From 5d41815f0386e3017a6300d401c919cca6f63b95 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 22:16:12 -0400 Subject: [PATCH 021/205] Uncomment tests. --- tests/gflownet/envs/test_ccube.py | 2184 ++++++++++++++--------------- 1 file changed, 1092 insertions(+), 1092 deletions(-) diff --git a/tests/gflownet/envs/test_ccube.py b/tests/gflownet/envs/test_ccube.py index aee2fe1fa..d97e715c3 100644 --- a/tests/gflownet/envs/test_ccube.py +++ b/tests/gflownet/envs/test_ccube.py @@ -64,1095 +64,1095 @@ def policy_output__as_expected(env, policy_outputs, params): ) -# @pytest.mark.parametrize("env", ["cube1d", "cube2d"]) -# def test__mask_forward__returns_all_true_if_done(env, request): -# env = request.getfixturevalue(env) -# # Sample states -# states = env.get_uniform_terminating_states(100) -# # Iterate over state and test -# for state in states: -# env.set_state(state, done=True) -# mask = env.get_mask_invalid_actions_forward() -# assert all(mask) -# -# -# @pytest.mark.parametrize("env", ["cube1d", "cube2d"]) -# def test__mask_backward__returns_all_true_except_eos_if_done(env, request): -# env = request.getfixturevalue(env) -# # Sample states -# states = env.get_uniform_terminating_states(100) -# # Iterate over state and test -# for state in states: -# env.set_state(state, done=True) -# mask = env.get_mask_invalid_actions_backward() -# assert all(mask[:2]) -# assert mask[2] is False -# -# -# @pytest.mark.parametrize( -# "state, mask_expected", -# [ -# ( -# [-1.0], -# [False, False, True, False], -# ), -# ( -# [0.0], -# [False, True, False, False], -# ), -# ( -# [0.5], -# [False, True, False, False], -# ), -# ( -# [0.90], -# [False, True, False, False], -# ), -# ( -# [0.95], -# [True, True, False, False], -# ), -# ], -# ) -# def test__mask_forward__1d__returns_expected(cube1d, state, mask_expected): -# env = cube1d -# mask = env.get_mask_invalid_actions_forward(state) -# assert mask == mask_expected -# -# -# @pytest.mark.parametrize( -# "state, mask_expected", -# [ -# ( -# [-1.0, -1.0], -# [False, False, True, False, False], -# ), -# ( -# [0.0, 0.0], -# [False, True, False, False, False], -# ), -# ( -# [0.5, 0.0], -# [False, True, False, False, False], -# ), -# ( -# [0.0, 0.01], -# [False, True, False, False, False], -# ), -# ( -# [0.5, 0.5], -# [False, True, False, False, False], -# ), -# ( -# [0.90, 0.5], -# [False, True, False, False, False], -# ), -# ( -# [0.95, 0.5], -# [True, True, False, False, False], -# ), -# ( -# [0.5, 0.90], -# [False, True, False, False, False], -# ), -# ( -# [0.5, 0.95], -# [True, True, False, False, False], -# ), -# ( -# [0.95, 0.95], -# [True, True, False, False, False], -# ), -# ], -# ) -# def test__mask_forward__2d__returns_expected(cube2d, state, mask_expected): -# env = cube2d -# mask = env.get_mask_invalid_actions_forward(state) -# assert mask == mask_expected -# -# -# @pytest.mark.parametrize( -# "state, mask_expected", -# [ -# ( -# [-1.0], -# [True, False, True, False], -# ), -# ( -# [0.0], -# [True, False, True, False], -# ), -# ( -# [0.05], -# [True, False, True, False], -# ), -# ( -# [0.1], -# [False, True, True, False], -# ), -# ( -# [0.5], -# [False, True, True, False], -# ), -# ( -# [0.90], -# [False, True, True, False], -# ), -# ( -# [0.95], -# [False, True, True, False], -# ), -# ], -# ) -# def test__mask_backward__1d__returns_expected(cube1d, state, mask_expected): -# env = cube1d -# mask = env.get_mask_invalid_actions_backward(state) -# assert mask == mask_expected -# -# -# @pytest.mark.parametrize( -# "state, mask_expected", -# [ -# ( -# [-1.0, -1.0], -# [True, False, True, False, False], -# ), -# ( -# [0.0, 0.0], -# [True, False, True, False, False], -# ), -# ( -# [0.5, 0.5], -# [False, True, True, False, False], -# ), -# ( -# [0.05, 0.5], -# [True, False, True, False, False], -# ), -# ( -# [0.5, 0.05], -# [True, False, True, False, False], -# ), -# ( -# [0.05, 0.05], -# [True, False, True, False, False], -# ), -# ( -# [0.90, 0.5], -# [False, True, True, False, False], -# ), -# ( -# [0.5, 0.90], -# [False, True, True, False, False], -# ), -# ( -# [0.95, 0.5], -# [False, True, True, False, False], -# ), -# ( -# [0.5, 0.95], -# [False, True, True, False, False], -# ), -# ( -# [0.95, 0.95], -# [False, True, True, False, False], -# ), -# ], -# ) -# def test__mask_backward__2d__returns_expected(cube2d, state, mask_expected): -# env = cube2d -# mask = env.get_mask_invalid_actions_backward(state) -# assert mask == mask_expected -# -# -# @pytest.mark.parametrize( -# "state, increments_rel, state_expected", -# [ -# ( -# [0.3, 0.5], -# [0.0, 0.0], -# [0.4, 0.6], -# ), -# ( -# [0.0, 0.0], -# [0.1794, 0.9589], -# [0.26146, 0.96301], -# ), -# ( -# [0.3, 0.5], -# [1.0, 1.0], -# [1.0, 1.0], -# ), -# ( -# [0.3, 0.5], -# [0.5, 0.5], -# [0.7, 0.8], -# ), -# ( -# [0.27, 0.85], -# [0.12, 0.76], -# [0.4456, 0.988], -# ), -# ], -# ) -# def test__relative_to_absolute_increments__2d_forward__returns_expected( -# cube2d, state, increments_rel, state_expected -# ): -# env = cube2d -# # Convert to tensors -# states = tfloat([state], float_type=env.float, device=env.device) -# increments_rel = tfloat([increments_rel], float_type=env.float, device=env.device) -# states_expected = tfloat([state_expected], float_type=env.float, device=env.device) -# # Get absolute increments -# increments_abs = env.relative_to_absolute_increments( -# states, increments_rel, is_backward=False -# ) -# states_next = states + increments_abs -# assert torch.all(torch.isclose(states_next, states_expected)) -# -# -# @pytest.mark.parametrize( -# "state, increments_rel, state_expected", -# [ -# ( -# [1.0, 1.0], -# [0.0, 0.0], -# [0.9, 0.9], -# ), -# ( -# [1.0, 1.0], -# [1.0, 1.0], -# [0.0, 0.0], -# ), -# ( -# [1.0, 1.0], -# [0.1794, 0.9589], -# [0.73854, 0.03699], -# ), -# ( -# [0.3, 0.5], -# [0.0, 0.0], -# [0.2, 0.4], -# ), -# ( -# [0.3, 0.5], -# [1.0, 1.0], -# [0.0, 0.0], -# ), -# ], -# ) -# def test__relative_to_absolute_increments__2d_backward__returns_expected( -# cube2d, state, increments_rel, state_expected -# ): -# env = cube2d -# # Convert to tensors -# states = tfloat([state], float_type=env.float, device=env.device) -# increments_rel = tfloat([increments_rel], float_type=env.float, device=env.device) -# states_expected = tfloat([state_expected], float_type=env.float, device=env.device) -# # Get absolute increments -# increments_abs = env.relative_to_absolute_increments( -# states, increments_rel, is_backward=True -# ) -# states_next = states - increments_abs -# assert torch.all(torch.isclose(states_next, states_expected)) -# -# -# @pytest.mark.parametrize( -# "state, action, state_expected", -# [ -# ( -# [-1.0, -1.0], -# (0.5, 0.5, 1.0), -# [0.5, 0.5], -# ), -# ( -# [-1.0, -1.0], -# (0.0, 0.0, 1.0), -# [0.0, 0.0], -# ), -# ( -# [-1.0, -1.0], -# (0.1794, 0.9589, 1.0), -# [0.1794, 0.9589], -# ), -# ( -# [0.0, 0.0], -# (0.1, 0.1, 0.0), -# [0.1, 0.1], -# ), -# ( -# [0.0, 0.0], -# (0.1794, 0.9589, 0.0), -# [0.1794, 0.9589], -# ), -# ( -# [0.3, 0.5], -# (0.1, 0.1, 0.0), -# [0.4, 0.6], -# ), -# ( -# [0.3, 0.5], -# (0.7, 0.5, 0.0), -# [1.0, 1.0], -# ), -# ( -# [0.3, 0.5], -# (0.4, 0.3, 0.0), -# [0.7, 0.8], -# ), -# ( -# [0.27, 0.85], -# (0.1756, 0.138, 0.0), -# [0.4456, 0.988], -# ), -# ( -# [0.45, 0.27], -# (np.inf, np.inf, np.inf), -# [0.45, 0.27], -# ), -# ( -# [0.0, 0.0], -# (np.inf, np.inf, np.inf), -# [0.0, 0.0], -# ), -# ], -# ) -# def test__step_forward__2d__returns_expected(cube2d, state, action, state_expected): -# env = cube2d -# env.set_state(state) -# state_new, action, valid = env.step(action) -# assert env.isclose(state_new, state_expected) -# -# -# @pytest.mark.parametrize( -# "state, action, state_expected", -# [ -# ( -# [0.5, 0.9], -# (0.3, 0.2, 0.0), -# [0.2, 0.7], -# ), -# ( -# [0.95, 0.4456], -# (0.1, 0.27, 0.0), -# [0.85, 0.1756], -# ), -# ( -# [0.1, 0.2], -# (0.1, 0.1, 0.0), -# [0.0, 0.1], -# ), -# ( -# [0.1, 0.2], -# (0.1, 0.2, 1.0), -# [-1.0, -1.0], -# ), -# ( -# [0.95, 0.0], -# (0.95, 0.0, 1.0), -# [-1.0, -1.0], -# ), -# ], -# ) -# def test__step_backward__2d__returns_expected(cube2d, state, action, state_expected): -# env = cube2d -# env.set_state(state) -# state_new, action, valid = env.step_backwards(action) -# assert env.isclose(state_new, state_expected) -# -# -# @pytest.mark.parametrize( -# "states, force_eos", -# [ -# ( -# [[-1.0, -1.0], [0.0, 0.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], -# [False, False, False, False, False], -# ), -# ( -# [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.0], [0.16, 0.93]], -# [False, False, False, False, False], -# ), -# ( -# [[0.05, 0.97], [0.56, 0.23], [0.95, 0.3], [0.2, 0.95], [0.01, 0.01]], -# [False, False, False, False, False], -# ), -# ( -# [[0.0, 0.0], [0.0, 0.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], -# [False, False, False, True, False], -# ), -# ( -# [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.0], [0.16, 0.93]], -# [False, True, True, False, False], -# ), -# ( -# [[0.05, 0.97], [0.56, 0.23], [0.95, 0.98], [0.92, 0.95], [0.01, 0.01]], -# [False, False, False, True, True], -# ), -# ], -# ) -# def test__sample_actions_forward__2d__returns_expected(cube2d, states, force_eos): -# env = cube2d -# n_states = len(states) -# force_eos = tbool(force_eos, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device -# ) -# # Define Beta distribution with low variance and get confident range -# n_samples = 10000 -# beta_params_min = 0.0 -# beta_params_max = 10000 -# alpha = 10 -# alphas_presigmoid = alpha * torch.ones(n_samples) -# alphas = beta_params_max * torch.sigmoid(alphas_presigmoid) + beta_params_min -# beta = 1.0 -# betas_presigmoid = beta * torch.ones(n_samples) -# betas = beta_params_max * torch.sigmoid(betas_presigmoid) + beta_params_min -# beta_distr = Beta(alphas, betas) -# samples = beta_distr.sample() -# mean_incr_rel = 0.9 * samples.mean() -# min_incr_rel = 0.9 * samples.min() -# max_incr_rel = 1.1 * samples.max() -# # Define Bernoulli parameters for EOS with deterministic probability -# logit_force_eos = torch.inf -# logit_force_noeos = -torch.inf -# # Estimate confident intervals of absolute actions -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# is_source = torch.all(states_torch == -1.0, dim=1) -# is_near_edge = states_torch > 1.0 - env.min_incr -# increments_min = torch.full_like( -# states_torch, min_incr_rel, dtype=env.float, device=env.device -# ) -# increments_max = torch.full_like( -# states_torch, max_incr_rel, dtype=env.float, device=env.device -# ) -# increments_min[~is_source] = env.relative_to_absolute_increments( -# states_torch[~is_source], increments_min[~is_source], is_backward=False -# ) -# increments_max[~is_source] = env.relative_to_absolute_increments( -# states_torch[~is_source], increments_max[~is_source], is_backward=False -# ) -# # Get EOS actions -# is_eos_forced = torch.any(is_near_edge, dim=1) -# is_eos = torch.logical_or(is_eos_forced, force_eos) -# increments_min[is_eos] = torch.inf -# increments_max[is_eos] = torch.inf -# # Reconfigure environment -# env.n_comp = 1 -# env.beta_params_min = beta_params_min -# env.beta_params_max = beta_params_max -# # Build policy outputs -# params = env.fixed_distr_params -# params["beta_alpha"] = alpha -# params["beta_beta"] = beta -# params["bernoulli_eos_logit"] = logit_force_noeos -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# policy_outputs[force_eos, -1] = logit_force_eos -# # Sample actions -# actions, _ = env.sample_actions_batch( -# policy_outputs, masks, states, is_backward=False -# ) -# actions_tensor = tfloat(actions, float_type=env.float, device=env.device) -# actions_eos = torch.all(actions_tensor == torch.inf, dim=1) -# assert torch.all(actions_eos == is_eos) -# assert torch.all(actions_tensor[:, :-1] >= increments_min) -# assert torch.all(actions_tensor[:, :-1] <= increments_max) -# -# -# @pytest.mark.parametrize( -# "states, force_bts", -# [ -# ( -# [[1.0, 1.0], [1.0, 1.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], -# [False, False, False, False, False], -# ), -# ( -# [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.05], [0.16, 0.93]], -# [False, False, False, False, False], -# ), -# ( -# [[0.05, 0.97], [0.56, 0.23], [0.95, 0.3], [0.2, 0.95], [0.01, 0.01]], -# [False, False, False, False, False], -# ), -# ( -# [[0.0001, 0.0], [0.001, 0.01], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], -# [False, False, False, True, False], -# ), -# ( -# [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [1.0, 1.0], [0.16, 0.93]], -# [False, True, True, True, False], -# ), -# ( -# [[0.05, 0.97], [0.56, 0.23], [0.95, 0.98], [0.92, 0.95], [0.01, 0.01]], -# [False, False, False, True, True], -# ), -# ], -# ) -# def test__sample_actions_backward__2d__returns_expected(cube2d, states, force_bts): -# env = cube2d -# n_states = len(states) -# force_bts = tbool(force_bts, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device -# ) -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# # Define Beta distribution with low variance and get confident range -# n_samples = 10000 -# beta_params_min = 0.0 -# beta_params_max = 10000 -# alpha = 10 -# alphas_presigmoid = alpha * torch.ones(n_samples) -# alphas = beta_params_max * torch.sigmoid(alphas_presigmoid) + beta_params_min -# beta = 1.0 -# betas_presigmoid = beta * torch.ones(n_samples) -# betas = beta_params_max * torch.sigmoid(betas_presigmoid) + beta_params_min -# beta_distr = Beta(alphas, betas) -# samples = beta_distr.sample() -# mean_incr_rel = 0.9 * samples.mean() -# min_incr_rel = 0.9 * samples.min() -# max_incr_rel = 1.1 * samples.max() -# # Define Bernoulli parameters for BTS with deterministic probability -# logit_force_bts = torch.inf -# logit_force_nobts = -torch.inf -# # Estimate confident intervals of absolute actions -# increments_min = torch.full_like( -# states_torch, min_incr_rel, dtype=env.float, device=env.device -# ) -# increments_max = torch.full_like( -# states_torch, max_incr_rel, dtype=env.float, device=env.device -# ) -# increments_min = env.relative_to_absolute_increments( -# states_torch, increments_min, is_backward=True -# ) -# increments_max = env.relative_to_absolute_increments( -# states_torch, increments_max, is_backward=True -# ) -# # Get BTS actions -# is_near_edge = states_torch < env.min_incr -# is_bts_forced = torch.any(is_near_edge, dim=1) -# is_bts = torch.logical_or(is_bts_forced, force_bts) -# increments_min[is_bts] = states_torch[is_bts] -# increments_max[is_bts] = states_torch[is_bts] -# # Reconfigure environment -# env.n_comp = 1 -# env.beta_params_min = beta_params_min -# env.beta_params_max = beta_params_max -# # Build policy outputs -# params = env.fixed_distr_params -# params["beta_alpha"] = alpha -# params["beta_beta"] = beta -# params["bernoulli_source_logit"] = logit_force_nobts -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# policy_outputs[force_bts, -2] = logit_force_bts -# # Sample actions -# actions, _ = env.sample_actions_batch( -# policy_outputs, masks, states, is_backward=True -# ) -# actions_tensor = tfloat(actions, float_type=env.float, device=env.device) -# actions_bts = torch.all(actions_tensor[:, :-1] == states_torch, dim=1) -# assert torch.all(actions_bts == is_bts) -# assert torch.all(actions_tensor[:, :-1] >= increments_min) -# assert torch.all(actions_tensor[:, :-1] <= increments_max) -# -# -# @pytest.mark.parametrize( -# "states, actions", -# [ -# ( -# [[0.95, 0.97], [0.96, 0.5], [0.5, 0.96]], -# [[0.02, 0.01, 0.0], [0.01, 0.2, 0.0], [0.3, 0.01, 0.0]], -# ), -# ( -# [[0.95, 0.97], [0.901, 0.5], [1.0, 1.0]], -# [[np.inf, np.inf, np.inf], [0.01, 0.2, 0.0], [0.3, 0.01, 0.0]], -# ), -# ], -# ) -# def test__get_logprobs_forward__2d__nearedge_returns_prob1(cube2d, states, actions): -# """ -# The only valid action from 'near-edge' states is EOS, thus the the log probability -# should be zero, regardless of the action and the policy outputs -# """ -# env = cube2d -# n_states = len(states) -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# actions = tfloat(actions, float_type=env.float, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device -# ) -# # Build policy outputs -# params = env.fixed_distr_params -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# # Add noise to policy outputs -# policy_outputs += torch.randn(policy_outputs.shape) -# # Get log probs -# logprobs = env.get_logprobs( -# policy_outputs, actions, masks, states_torch, is_backward=False -# ) -# assert torch.all(logprobs == 0.0) -# -# -# @pytest.mark.parametrize( -# "states, actions", -# [ -# ( -# [[0.1, 0.2], [0.3, 0.5], [0.5, 0.95]], -# [ -# [np.inf, np.inf, np.inf], -# [np.inf, np.inf, np.inf], -# [np.inf, np.inf, np.inf], -# ], -# ), -# ( -# [[1.0, 1.0], [0.01, 0.01], [0.001, 0.1]], -# [ -# [np.inf, np.inf, np.inf], -# [np.inf, np.inf, np.inf], -# [np.inf, np.inf, np.inf], -# ], -# ), -# ], -# ) -# def test__get_logprobs_forward__2d__eos_actions_return_expected( -# cube2d, states, actions -# ): -# """ -# The only valid action from 'near-edge' states is EOS, thus the the log probability -# should be zero, regardless of the action and the policy outputs -# """ -# env = cube2d -# n_states = len(states) -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# actions = tfloat(actions, float_type=env.float, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device -# ) -# # Get EOS forced -# is_near_edge = states_torch > 1.0 - env.min_incr -# is_eos_forced = torch.any(is_near_edge, dim=1) -# # Define Bernoulli parameter for EOS -# # If Bernouilli has logit torch.inf, the logprobs are nan -# logit_eos = 1 -# distr_eos = Bernoulli(logits=logit_eos) -# logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) -# # Build policy outputs -# params = env.fixed_distr_params -# params["bernoulli_eos_logit"] = logit_eos -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# # Get log probs -# logprobs = env.get_logprobs( -# policy_outputs, actions, masks, states_torch, is_backward=False -# ) -# assert torch.all(logprobs[is_eos_forced] == 0.0) -# assert torch.all(torch.isclose(logprobs[~is_eos_forced], logprob_eos, atol=1e-6)) -# -# -# @pytest.mark.parametrize( -# "actions", -# [ -# [[0.1, 0.2, 1.0], [0.3, 0.5, 1.0], [0.5, 0.95, 1.0]], -# [[0.999, 0.999, 1.0], [0.0001, 0.0001, 1.0], [0.5, 0.5, 1.0]], -# [[0.0, 0.0, 1.0], [1.0, 1.0, 1.0]], -# ], -# ) -# def test__get_logprobs_forward__2d__all_actions_from_source_uniform_policy_prob1( -# cube2d, actions -# ): -# """ -# With Uniform increment policy, all the actions from the source must have the same -# probability. -# """ -# env = cube2d -# n_states = len(actions) -# states = [env.source for _ in range(n_states)] -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# actions = tfloat(actions, float_type=env.float, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device -# ) -# # Define Uniform Beta distribution (large values of alpha and beta and max of 1.0) -# beta_params_min = 0.0 -# beta_params_max = 1.0 -# alpha_presigmoid = 1000.0 -# betas_presigmoid = 1000.0 -# # Define Bernoulli parameter for impossible EOS -# # If Bernouilli has logit -torch.inf, the logprobs are nan -# logit_force_noeos = -1000 -# # Reconfigure environment -# env.n_comp = 1 -# env.beta_params_min = beta_params_min -# env.beta_params_max = beta_params_max -# # Build policy outputs -# params = env.fixed_distr_params -# params["beta_alpha"] = alpha_presigmoid -# params["beta_beta"] = betas_presigmoid -# params["bernoulli_eos_logit"] = logit_force_noeos -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# # Get log probs -# logprobs = env.get_logprobs( -# policy_outputs, actions, masks, states_torch, is_backward=False -# ) -# assert torch.all(logprobs == 0.0) -# -# -# @pytest.mark.parametrize( -# "states, actions", -# [ -# ( -# [[0.2, 0.2], [0.5, 0.5], [0.7, 0.7]], -# [[0.1, 0.1, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], -# ), -# ( -# [[0.6384, 0.4577], [0.5, 0.5], [0.7, 0.7]], -# [[0.2988, 0.3585, 0.0], [0.2, 0.3, 0.0], [0.11, 0.1001, 0.0]], -# ), -# ( -# [[-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0]], -# [[0.2988, 0.3585, 1.0], [0.2, 0.3, 1.0], [0.11, 0.1001, 1.0]], -# ), -# ( -# [[0.6384, 0.4577], [0.5, 0.5], [0.7, 0.7]], -# [[0.2988, 0.3585, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], -# ), -# ( -# [[0.0, 0.0], [-1.0, -1.0], [0.0, 0.0]], -# [[0.1, 0.2, 0.0], [0.001, 0.001, 1.0], [0.5, 0.5, 0.0]], -# ), -# ], -# ) -# def test__get_logprobs_forward__2d__finite(cube2d, states, actions): -# env = cube2d -# n_states = len(states) -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# actions = tfloat(actions, float_type=env.float, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device -# ) -# # Get EOS forced -# is_near_edge = states_torch > 1.0 - env.min_incr -# is_eos_forced = torch.any(is_near_edge, dim=1) -# # Define Bernoulli parameter for EOS -# # If Bernouilli has logit torch.inf, the logprobs are nan -# logit_eos = 1 -# distr_eos = Bernoulli(logits=logit_eos) -# logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) -# # Build policy outputs -# params = env.fixed_distr_params -# params["bernoulli_eos_logit"] = logit_eos -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# # Get log probs -# logprobs = env.get_logprobs( -# policy_outputs, actions, masks, states_torch, is_backward=False -# ) -# assert torch.all(logprobs[is_eos_forced] == 0.0) -# assert torch.all(torch.isfinite(logprobs)) -# -# -# @pytest.mark.parametrize( -# "states, actions", -# [ -# ( -# [[0.2, 0.2], [0.5, 0.5], [-1.0, -1.0], [-1.0, -1.0], [0.95, 0.95]], -# [ -# [0.5, 0.5, 0.0], -# [0.3, 0.3, 0.0], -# [0.3, 0.3, 1.0], -# [0.5, 0.5, 1.0], -# [np.inf, np.inf, np.inf], -# ], -# ), -# ], -# ) -# def test__get_logprobs_forward__2d__is_finite(cube2d, states, actions): -# env = cube2d -# n_states = len(states) -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# actions = tfloat(actions, float_type=env.float, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device -# ) -# # Get EOS forced -# is_near_edge = states_torch > 1.0 - env.min_incr -# is_eos_forced = torch.any(is_near_edge, dim=1) -# # Define Bernoulli parameter for EOS -# # If Bernouilli has logit torch.inf, the logprobs are nan -# logit_eos = 1 -# distr_eos = Bernoulli(logits=logit_eos) -# logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) -# # Build policy outputs -# params = env.fixed_distr_params -# params["bernoulli_eos_logit"] = logit_eos -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# # Get log probs -# logprobs = env.get_logprobs( -# policy_outputs, actions, masks, states_torch, is_backward=False -# ) -# assert torch.all(torch.isfinite(logprobs)) -# -# -# @pytest.mark.parametrize( -# "states, actions", -# [ -# ( -# [[0.3, 0.3], [0.5, 0.5], [1.0, 1.0], [0.05, 0.2], [0.05, 0.05]], -# [ -# [0.2, 0.2, 0.0], -# [0.2, 0.2, 0.0], -# [0.5, 0.5, 0.0], -# [0.05, 0.2, 1.0], -# [0.05, 0.05, 1.0], -# ], -# ), -# ], -# ) -# def test__get_logprobs_backward__2d__is_finite(cube2d, states, actions): -# env = cube2d -# n_states = len(states) -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# actions = tfloat(actions, float_type=env.float, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device -# ) -# # Define Bernoulli parameter for BTS -# # If Bernouilli has logit torch.inf, the logprobs are nan -# logit_bts = 1 -# distr_bts = Bernoulli(logits=logit_bts) -# logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) -# # Build policy outputs -# params = env.fixed_distr_params -# params["bernoulli_source_logit"] = logit_bts -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# # Get log probs -# logprobs = env.get_logprobs( -# policy_outputs, actions, masks, states_torch, is_backward=True -# ) -# assert torch.all(torch.isfinite(logprobs)) -# -# -# @pytest.mark.parametrize( -# "states, actions", -# [ -# ( -# [[0.02, 0.01], [0.01, 0.2], [0.3, 0.01]], -# [[0.02, 0.01, 1.0], [0.01, 0.2, 1.0], [0.3, 0.01, 1.0]], -# ), -# ( -# [[0.0, 0.0], [0.0, 0.2], [0.3, 0.0]], -# [[0.0, 0.0, 1.0], [0.0, 0.2, 1.0], [0.3, 0.0, 1.0]], -# ), -# ], -# ) -# def test__get_logprobs_backward__2d__nearedge_returns_prob1(cube2d, states, actions): -# """ -# The only valid backward action from 'near-edge' states is BTS, thus the the log -# probability should be zero. -# """ -# env = cube2d -# n_states = len(states) -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# actions = tfloat(actions, float_type=env.float, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device -# ) -# # Build policy outputs -# params = env.fixed_distr_params -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# # Add noise to policy outputs -# policy_outputs += torch.randn(policy_outputs.shape) -# # Get log probs -# logprobs = env.get_logprobs( -# policy_outputs, actions, masks, states_torch, is_backward=True -# ) -# assert torch.all(logprobs == 0.0) -# -# -# @pytest.mark.parametrize( -# "states, actions", -# [ -# ( -# [[0.1, 0.2], [0.3, 0.5], [0.5, 0.95]], -# [[0.1, 0.2, 1.0], [0.3, 0.5, 1.0], [0.5, 0.95, 1.0]], -# ), -# ( -# [[0.99, 0.99], [0.01, 0.01], [0.001, 0.1]], -# [[0.99, 0.99, 1.0], [0.01, 0.01, 1.0], [0.001, 0.1, 1.0]], -# ), -# ( -# [[1.0, 1.0], [0.0, 0.0]], -# [[1.0, 1.0, 1.0], [0.0, 0.0, 1.0]], -# ), -# ], -# ) -# def test__get_logprobs_backward__2d__bts_actions_return_expected( -# cube2d, states, actions -# ): -# """ -# The only valid action from 'near-edge' states is BTS, thus the log probability -# should be zero, regardless of the action and the policy outputs -# """ -# env = cube2d -# n_states = len(states) -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# actions = tfloat(actions, float_type=env.float, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device -# ) -# # Get BTS forced -# is_near_edge = states_torch < env.min_incr -# is_bts_forced = torch.any(is_near_edge, dim=1) -# # Define Bernoulli parameter for BTS -# # If Bernouilli has logit torch.inf, the logprobs are nan -# logit_bts = 1 -# distr_bts = Bernoulli(logits=logit_bts) -# logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) -# # Build policy outputs -# params = env.fixed_distr_params -# params["bernoulli_source_logit"] = logit_bts -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# # Get log probs -# logprobs = env.get_logprobs( -# policy_outputs, actions, masks, states_torch, is_backward=True -# ) -# assert torch.all(logprobs[is_bts_forced] == 0.0) -# assert torch.all(torch.isclose(logprobs[~is_bts_forced], logprob_bts, atol=1e-6)) -# -# -# @pytest.mark.parametrize( -# "states, actions", -# [ -# ( -# [[0.3, 0.3], [0.5, 0.5], [0.8, 0.8]], -# [[0.2, 0.2, 0.0], [0.2, 0.2, 0.0], [0.2, 0.2, 0.0]], -# ), -# ( -# [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], -# [[0.2, 0.2, 0.0], [0.2, 0.2, 0.0], [0.2, 0.2, 0.0]], -# ), -# ( -# [[1.0, 1.0], [0.5, 0.5], [0.3, 0.3]], -# [[0.1, 0.1, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], -# ), -# ], -# ) -# def test__get_logprobs_backward__2d__notnan(cube2d, states, actions): -# env = cube2d -# n_states = len(states) -# states_torch = tfloat(states, float_type=env.float, device=env.device) -# actions = tfloat(actions, float_type=env.float, device=env.device) -# # Get masks -# masks = tbool( -# [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device -# ) -# # Get BTS forced -# is_near_edge = states_torch < env.min_incr -# is_bts_forced = torch.any(is_near_edge, dim=1) -# # Define Bernoulli parameter for BTS -# # If Bernouilli has logit torch.inf, the logprobs are nan -# logit_bts = 1 -# distr_bts = Bernoulli(logits=logit_bts) -# logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) -# # Build policy outputs -# params = env.fixed_distr_params -# params["bernoulli_source_logit"] = logit_bts -# policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) -# # Get log probs -# logprobs = env.get_logprobs( -# policy_outputs, actions, masks, states_torch, is_backward=True -# ) -# assert torch.all(logprobs[is_bts_forced] == 0.0) -# assert torch.all(torch.isfinite(logprobs)) -# -# -# @pytest.mark.parametrize( -# "state, expected", -# [ -# ( -# [0.0, 0.0], -# [0.0, 0.0], -# ), -# ( -# [1.0, 1.0], -# [1.0, 1.0], -# ), -# ( -# [1.1, 1.00001], -# [1.0, 1.0], -# ), -# ( -# [-0.1, 1.00001], -# [0.0, 1.0], -# ), -# ( -# [0.1, 0.21], -# [0.1, 0.21], -# ), -# ], -# ) -# @pytest.mark.skip(reason="skip while developping other tests") -# def test__state2policy_returns_expected(env, state, expected): -# assert env.state2policy(state) == expected -# -# -# @pytest.mark.parametrize( -# "states, expected", -# [ -# ( -# [[0.0, 0.0], [1.0, 1.0], [1.1, 1.00001], [-0.1, 1.00001], [0.1, 0.21]], -# [[0.0, 0.0], [1.0, 1.0], [1.0, 1.0], [0.0, 1.0], [0.1, 0.21]], -# ), -# ], -# ) -# @pytest.mark.skip(reason="skip while developping other tests") -# def test__statetorch2policy_returns_expected(env, states, expected): -# assert torch.equal( -# env.statetorch2policy(torch.tensor(states)), torch.tensor(expected) -# ) -# -# -# @pytest.mark.parametrize( -# "state, expected", -# [ -# ( -# [0.0, 0.0], -# [True, False, False], -# ), -# ( -# [0.1, 0.1], -# [False, True, False], -# ), -# ( -# [1.0, 0.0], -# [False, True, False], -# ), -# ( -# [1.1, 0.0], -# [True, True, False], -# ), -# ( -# [0.1, 1.1], -# [True, True, False], -# ), -# ], -# ) -# @pytest.mark.skip(reason="skip while developping other tests") -# def test__get_mask_invalid_actions_forward__returns_expected(env, state, expected): -# assert env.get_mask_invalid_actions_forward(state) == expected, print( -# state, expected, env.get_mask_invalid_actions_forward(state) -# ) -# -# -# def test__continuous_env_common__cube1d(cube1d): -# return common.test__continuous_env_common(cube1d) -# -# -# def test__continuous_env_common__cube2d(cube2d): -# return common.test__continuous_env_common(cube2d) +@pytest.mark.parametrize("env", ["cube1d", "cube2d"]) +def test__mask_forward__returns_all_true_if_done(env, request): + env = request.getfixturevalue(env) + # Sample states + states = env.get_uniform_terminating_states(100) + # Iterate over state and test + for state in states: + env.set_state(state, done=True) + mask = env.get_mask_invalid_actions_forward() + assert all(mask) + + +@pytest.mark.parametrize("env", ["cube1d", "cube2d"]) +def test__mask_backward__returns_all_true_except_eos_if_done(env, request): + env = request.getfixturevalue(env) + # Sample states + states = env.get_uniform_terminating_states(100) + # Iterate over state and test + for state in states: + env.set_state(state, done=True) + mask = env.get_mask_invalid_actions_backward() + assert all(mask[:2]) + assert mask[2] is False + + +@pytest.mark.parametrize( + "state, mask_expected", + [ + ( + [-1.0], + [False, False, True, False], + ), + ( + [0.0], + [False, True, False, False], + ), + ( + [0.5], + [False, True, False, False], + ), + ( + [0.90], + [False, True, False, False], + ), + ( + [0.95], + [True, True, False, False], + ), + ], +) +def test__mask_forward__1d__returns_expected(cube1d, state, mask_expected): + env = cube1d + mask = env.get_mask_invalid_actions_forward(state) + assert mask == mask_expected + + +@pytest.mark.parametrize( + "state, mask_expected", + [ + ( + [-1.0, -1.0], + [False, False, True, False, False], + ), + ( + [0.0, 0.0], + [False, True, False, False, False], + ), + ( + [0.5, 0.0], + [False, True, False, False, False], + ), + ( + [0.0, 0.01], + [False, True, False, False, False], + ), + ( + [0.5, 0.5], + [False, True, False, False, False], + ), + ( + [0.90, 0.5], + [False, True, False, False, False], + ), + ( + [0.95, 0.5], + [True, True, False, False, False], + ), + ( + [0.5, 0.90], + [False, True, False, False, False], + ), + ( + [0.5, 0.95], + [True, True, False, False, False], + ), + ( + [0.95, 0.95], + [True, True, False, False, False], + ), + ], +) +def test__mask_forward__2d__returns_expected(cube2d, state, mask_expected): + env = cube2d + mask = env.get_mask_invalid_actions_forward(state) + assert mask == mask_expected + + +@pytest.mark.parametrize( + "state, mask_expected", + [ + ( + [-1.0], + [True, False, True, False], + ), + ( + [0.0], + [True, False, True, False], + ), + ( + [0.05], + [True, False, True, False], + ), + ( + [0.1], + [False, True, True, False], + ), + ( + [0.5], + [False, True, True, False], + ), + ( + [0.90], + [False, True, True, False], + ), + ( + [0.95], + [False, True, True, False], + ), + ], +) +def test__mask_backward__1d__returns_expected(cube1d, state, mask_expected): + env = cube1d + mask = env.get_mask_invalid_actions_backward(state) + assert mask == mask_expected + + +@pytest.mark.parametrize( + "state, mask_expected", + [ + ( + [-1.0, -1.0], + [True, False, True, False, False], + ), + ( + [0.0, 0.0], + [True, False, True, False, False], + ), + ( + [0.5, 0.5], + [False, True, True, False, False], + ), + ( + [0.05, 0.5], + [True, False, True, False, False], + ), + ( + [0.5, 0.05], + [True, False, True, False, False], + ), + ( + [0.05, 0.05], + [True, False, True, False, False], + ), + ( + [0.90, 0.5], + [False, True, True, False, False], + ), + ( + [0.5, 0.90], + [False, True, True, False, False], + ), + ( + [0.95, 0.5], + [False, True, True, False, False], + ), + ( + [0.5, 0.95], + [False, True, True, False, False], + ), + ( + [0.95, 0.95], + [False, True, True, False, False], + ), + ], +) +def test__mask_backward__2d__returns_expected(cube2d, state, mask_expected): + env = cube2d + mask = env.get_mask_invalid_actions_backward(state) + assert mask == mask_expected + + +@pytest.mark.parametrize( + "state, increments_rel, state_expected", + [ + ( + [0.3, 0.5], + [0.0, 0.0], + [0.4, 0.6], + ), + ( + [0.0, 0.0], + [0.1794, 0.9589], + [0.26146, 0.96301], + ), + ( + [0.3, 0.5], + [1.0, 1.0], + [1.0, 1.0], + ), + ( + [0.3, 0.5], + [0.5, 0.5], + [0.7, 0.8], + ), + ( + [0.27, 0.85], + [0.12, 0.76], + [0.4456, 0.988], + ), + ], +) +def test__relative_to_absolute_increments__2d_forward__returns_expected( + cube2d, state, increments_rel, state_expected +): + env = cube2d + # Convert to tensors + states = tfloat([state], float_type=env.float, device=env.device) + increments_rel = tfloat([increments_rel], float_type=env.float, device=env.device) + states_expected = tfloat([state_expected], float_type=env.float, device=env.device) + # Get absolute increments + increments_abs = env.relative_to_absolute_increments( + states, increments_rel, is_backward=False + ) + states_next = states + increments_abs + assert torch.all(torch.isclose(states_next, states_expected)) + + +@pytest.mark.parametrize( + "state, increments_rel, state_expected", + [ + ( + [1.0, 1.0], + [0.0, 0.0], + [0.9, 0.9], + ), + ( + [1.0, 1.0], + [1.0, 1.0], + [0.0, 0.0], + ), + ( + [1.0, 1.0], + [0.1794, 0.9589], + [0.73854, 0.03699], + ), + ( + [0.3, 0.5], + [0.0, 0.0], + [0.2, 0.4], + ), + ( + [0.3, 0.5], + [1.0, 1.0], + [0.0, 0.0], + ), + ], +) +def test__relative_to_absolute_increments__2d_backward__returns_expected( + cube2d, state, increments_rel, state_expected +): + env = cube2d + # Convert to tensors + states = tfloat([state], float_type=env.float, device=env.device) + increments_rel = tfloat([increments_rel], float_type=env.float, device=env.device) + states_expected = tfloat([state_expected], float_type=env.float, device=env.device) + # Get absolute increments + increments_abs = env.relative_to_absolute_increments( + states, increments_rel, is_backward=True + ) + states_next = states - increments_abs + assert torch.all(torch.isclose(states_next, states_expected)) + + +@pytest.mark.parametrize( + "state, action, state_expected", + [ + ( + [-1.0, -1.0], + (0.5, 0.5, 1.0), + [0.5, 0.5], + ), + ( + [-1.0, -1.0], + (0.0, 0.0, 1.0), + [0.0, 0.0], + ), + ( + [-1.0, -1.0], + (0.1794, 0.9589, 1.0), + [0.1794, 0.9589], + ), + ( + [0.0, 0.0], + (0.1, 0.1, 0.0), + [0.1, 0.1], + ), + ( + [0.0, 0.0], + (0.1794, 0.9589, 0.0), + [0.1794, 0.9589], + ), + ( + [0.3, 0.5], + (0.1, 0.1, 0.0), + [0.4, 0.6], + ), + ( + [0.3, 0.5], + (0.7, 0.5, 0.0), + [1.0, 1.0], + ), + ( + [0.3, 0.5], + (0.4, 0.3, 0.0), + [0.7, 0.8], + ), + ( + [0.27, 0.85], + (0.1756, 0.138, 0.0), + [0.4456, 0.988], + ), + ( + [0.45, 0.27], + (np.inf, np.inf, np.inf), + [0.45, 0.27], + ), + ( + [0.0, 0.0], + (np.inf, np.inf, np.inf), + [0.0, 0.0], + ), + ], +) +def test__step_forward__2d__returns_expected(cube2d, state, action, state_expected): + env = cube2d + env.set_state(state) + state_new, action, valid = env.step(action) + assert env.isclose(state_new, state_expected) + + +@pytest.mark.parametrize( + "state, action, state_expected", + [ + ( + [0.5, 0.9], + (0.3, 0.2, 0.0), + [0.2, 0.7], + ), + ( + [0.95, 0.4456], + (0.1, 0.27, 0.0), + [0.85, 0.1756], + ), + ( + [0.1, 0.2], + (0.1, 0.1, 0.0), + [0.0, 0.1], + ), + ( + [0.1, 0.2], + (0.1, 0.2, 1.0), + [-1.0, -1.0], + ), + ( + [0.95, 0.0], + (0.95, 0.0, 1.0), + [-1.0, -1.0], + ), + ], +) +def test__step_backward__2d__returns_expected(cube2d, state, action, state_expected): + env = cube2d + env.set_state(state) + state_new, action, valid = env.step_backwards(action) + assert env.isclose(state_new, state_expected) + + +@pytest.mark.parametrize( + "states, force_eos", + [ + ( + [[-1.0, -1.0], [0.0, 0.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], + [False, False, False, False, False], + ), + ( + [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.0], [0.16, 0.93]], + [False, False, False, False, False], + ), + ( + [[0.05, 0.97], [0.56, 0.23], [0.95, 0.3], [0.2, 0.95], [0.01, 0.01]], + [False, False, False, False, False], + ), + ( + [[0.0, 0.0], [0.0, 0.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], + [False, False, False, True, False], + ), + ( + [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.0], [0.16, 0.93]], + [False, True, True, False, False], + ), + ( + [[0.05, 0.97], [0.56, 0.23], [0.95, 0.98], [0.92, 0.95], [0.01, 0.01]], + [False, False, False, True, True], + ), + ], +) +def test__sample_actions_forward__2d__returns_expected(cube2d, states, force_eos): + env = cube2d + n_states = len(states) + force_eos = tbool(force_eos, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device + ) + # Define Beta distribution with low variance and get confident range + n_samples = 10000 + beta_params_min = 0.0 + beta_params_max = 10000 + alpha = 10 + alphas_presigmoid = alpha * torch.ones(n_samples) + alphas = beta_params_max * torch.sigmoid(alphas_presigmoid) + beta_params_min + beta = 1.0 + betas_presigmoid = beta * torch.ones(n_samples) + betas = beta_params_max * torch.sigmoid(betas_presigmoid) + beta_params_min + beta_distr = Beta(alphas, betas) + samples = beta_distr.sample() + mean_incr_rel = 0.9 * samples.mean() + min_incr_rel = 0.9 * samples.min() + max_incr_rel = 1.1 * samples.max() + # Define Bernoulli parameters for EOS with deterministic probability + logit_force_eos = torch.inf + logit_force_noeos = -torch.inf + # Estimate confident intervals of absolute actions + states_torch = tfloat(states, float_type=env.float, device=env.device) + is_source = torch.all(states_torch == -1.0, dim=1) + is_near_edge = states_torch > 1.0 - env.min_incr + increments_min = torch.full_like( + states_torch, min_incr_rel, dtype=env.float, device=env.device + ) + increments_max = torch.full_like( + states_torch, max_incr_rel, dtype=env.float, device=env.device + ) + increments_min[~is_source] = env.relative_to_absolute_increments( + states_torch[~is_source], increments_min[~is_source], is_backward=False + ) + increments_max[~is_source] = env.relative_to_absolute_increments( + states_torch[~is_source], increments_max[~is_source], is_backward=False + ) + # Get EOS actions + is_eos_forced = torch.any(is_near_edge, dim=1) + is_eos = torch.logical_or(is_eos_forced, force_eos) + increments_min[is_eos] = torch.inf + increments_max[is_eos] = torch.inf + # Reconfigure environment + env.n_comp = 1 + env.beta_params_min = beta_params_min + env.beta_params_max = beta_params_max + # Build policy outputs + params = env.fixed_distr_params + params["beta_alpha"] = alpha + params["beta_beta"] = beta + params["bernoulli_eos_logit"] = logit_force_noeos + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + policy_outputs[force_eos, -1] = logit_force_eos + # Sample actions + actions, _ = env.sample_actions_batch( + policy_outputs, masks, states, is_backward=False + ) + actions_tensor = tfloat(actions, float_type=env.float, device=env.device) + actions_eos = torch.all(actions_tensor == torch.inf, dim=1) + assert torch.all(actions_eos == is_eos) + assert torch.all(actions_tensor[:, :-1] >= increments_min) + assert torch.all(actions_tensor[:, :-1] <= increments_max) + + +@pytest.mark.parametrize( + "states, force_bts", + [ + ( + [[1.0, 1.0], [1.0, 1.0], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], + [False, False, False, False, False], + ), + ( + [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [0.0, 0.05], [0.16, 0.93]], + [False, False, False, False, False], + ), + ( + [[0.05, 0.97], [0.56, 0.23], [0.95, 0.3], [0.2, 0.95], [0.01, 0.01]], + [False, False, False, False, False], + ), + ( + [[0.0001, 0.0], [0.001, 0.01], [0.3, 0.5], [0.27, 0.85], [0.56, 0.23]], + [False, False, False, True, False], + ), + ( + [[0.12, 0.17], [0.56, 0.23], [0.9, 0.9], [1.0, 1.0], [0.16, 0.93]], + [False, True, True, True, False], + ), + ( + [[0.05, 0.97], [0.56, 0.23], [0.95, 0.98], [0.92, 0.95], [0.01, 0.01]], + [False, False, False, True, True], + ), + ], +) +def test__sample_actions_backward__2d__returns_expected(cube2d, states, force_bts): + env = cube2d + n_states = len(states) + force_bts = tbool(force_bts, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device + ) + states_torch = tfloat(states, float_type=env.float, device=env.device) + # Define Beta distribution with low variance and get confident range + n_samples = 10000 + beta_params_min = 0.0 + beta_params_max = 10000 + alpha = 10 + alphas_presigmoid = alpha * torch.ones(n_samples) + alphas = beta_params_max * torch.sigmoid(alphas_presigmoid) + beta_params_min + beta = 1.0 + betas_presigmoid = beta * torch.ones(n_samples) + betas = beta_params_max * torch.sigmoid(betas_presigmoid) + beta_params_min + beta_distr = Beta(alphas, betas) + samples = beta_distr.sample() + mean_incr_rel = 0.9 * samples.mean() + min_incr_rel = 0.9 * samples.min() + max_incr_rel = 1.1 * samples.max() + # Define Bernoulli parameters for BTS with deterministic probability + logit_force_bts = torch.inf + logit_force_nobts = -torch.inf + # Estimate confident intervals of absolute actions + increments_min = torch.full_like( + states_torch, min_incr_rel, dtype=env.float, device=env.device + ) + increments_max = torch.full_like( + states_torch, max_incr_rel, dtype=env.float, device=env.device + ) + increments_min = env.relative_to_absolute_increments( + states_torch, increments_min, is_backward=True + ) + increments_max = env.relative_to_absolute_increments( + states_torch, increments_max, is_backward=True + ) + # Get BTS actions + is_near_edge = states_torch < env.min_incr + is_bts_forced = torch.any(is_near_edge, dim=1) + is_bts = torch.logical_or(is_bts_forced, force_bts) + increments_min[is_bts] = states_torch[is_bts] + increments_max[is_bts] = states_torch[is_bts] + # Reconfigure environment + env.n_comp = 1 + env.beta_params_min = beta_params_min + env.beta_params_max = beta_params_max + # Build policy outputs + params = env.fixed_distr_params + params["beta_alpha"] = alpha + params["beta_beta"] = beta + params["bernoulli_source_logit"] = logit_force_nobts + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + policy_outputs[force_bts, -2] = logit_force_bts + # Sample actions + actions, _ = env.sample_actions_batch( + policy_outputs, masks, states, is_backward=True + ) + actions_tensor = tfloat(actions, float_type=env.float, device=env.device) + actions_bts = torch.all(actions_tensor[:, :-1] == states_torch, dim=1) + assert torch.all(actions_bts == is_bts) + assert torch.all(actions_tensor[:, :-1] >= increments_min) + assert torch.all(actions_tensor[:, :-1] <= increments_max) + + +@pytest.mark.parametrize( + "states, actions", + [ + ( + [[0.95, 0.97], [0.96, 0.5], [0.5, 0.96]], + [[0.02, 0.01, 0.0], [0.01, 0.2, 0.0], [0.3, 0.01, 0.0]], + ), + ( + [[0.95, 0.97], [0.901, 0.5], [1.0, 1.0]], + [[np.inf, np.inf, np.inf], [0.01, 0.2, 0.0], [0.3, 0.01, 0.0]], + ), + ], +) +def test__get_logprobs_forward__2d__nearedge_returns_prob1(cube2d, states, actions): + """ + The only valid action from 'near-edge' states is EOS, thus the the log probability + should be zero, regardless of the action and the policy outputs + """ + env = cube2d + n_states = len(states) + states_torch = tfloat(states, float_type=env.float, device=env.device) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device + ) + # Build policy outputs + params = env.fixed_distr_params + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Add noise to policy outputs + policy_outputs += torch.randn(policy_outputs.shape) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states_torch, is_backward=False + ) + assert torch.all(logprobs == 0.0) + + +@pytest.mark.parametrize( + "states, actions", + [ + ( + [[0.1, 0.2], [0.3, 0.5], [0.5, 0.95]], + [ + [np.inf, np.inf, np.inf], + [np.inf, np.inf, np.inf], + [np.inf, np.inf, np.inf], + ], + ), + ( + [[1.0, 1.0], [0.01, 0.01], [0.001, 0.1]], + [ + [np.inf, np.inf, np.inf], + [np.inf, np.inf, np.inf], + [np.inf, np.inf, np.inf], + ], + ), + ], +) +def test__get_logprobs_forward__2d__eos_actions_return_expected( + cube2d, states, actions +): + """ + The only valid action from 'near-edge' states is EOS, thus the the log probability + should be zero, regardless of the action and the policy outputs + """ + env = cube2d + n_states = len(states) + states_torch = tfloat(states, float_type=env.float, device=env.device) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device + ) + # Get EOS forced + is_near_edge = states_torch > 1.0 - env.min_incr + is_eos_forced = torch.any(is_near_edge, dim=1) + # Define Bernoulli parameter for EOS + # If Bernouilli has logit torch.inf, the logprobs are nan + logit_eos = 1 + distr_eos = Bernoulli(logits=logit_eos) + logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) + # Build policy outputs + params = env.fixed_distr_params + params["bernoulli_eos_logit"] = logit_eos + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states_torch, is_backward=False + ) + assert torch.all(logprobs[is_eos_forced] == 0.0) + assert torch.all(torch.isclose(logprobs[~is_eos_forced], logprob_eos, atol=1e-6)) + + +@pytest.mark.parametrize( + "actions", + [ + [[0.1, 0.2, 1.0], [0.3, 0.5, 1.0], [0.5, 0.95, 1.0]], + [[0.999, 0.999, 1.0], [0.0001, 0.0001, 1.0], [0.5, 0.5, 1.0]], + [[0.0, 0.0, 1.0], [1.0, 1.0, 1.0]], + ], +) +def test__get_logprobs_forward__2d__all_actions_from_source_uniform_policy_prob1( + cube2d, actions +): + """ + With Uniform increment policy, all the actions from the source must have the same + probability. + """ + env = cube2d + n_states = len(actions) + states = [env.source for _ in range(n_states)] + states_torch = tfloat(states, float_type=env.float, device=env.device) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device + ) + # Define Uniform Beta distribution (large values of alpha and beta and max of 1.0) + beta_params_min = 0.0 + beta_params_max = 1.0 + alpha_presigmoid = 1000.0 + betas_presigmoid = 1000.0 + # Define Bernoulli parameter for impossible EOS + # If Bernouilli has logit -torch.inf, the logprobs are nan + logit_force_noeos = -1000 + # Reconfigure environment + env.n_comp = 1 + env.beta_params_min = beta_params_min + env.beta_params_max = beta_params_max + # Build policy outputs + params = env.fixed_distr_params + params["beta_alpha"] = alpha_presigmoid + params["beta_beta"] = betas_presigmoid + params["bernoulli_eos_logit"] = logit_force_noeos + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states_torch, is_backward=False + ) + assert torch.all(logprobs == 0.0) + + +@pytest.mark.parametrize( + "states, actions", + [ + ( + [[0.2, 0.2], [0.5, 0.5], [0.7, 0.7]], + [[0.1, 0.1, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], + ), + ( + [[0.6384, 0.4577], [0.5, 0.5], [0.7, 0.7]], + [[0.2988, 0.3585, 0.0], [0.2, 0.3, 0.0], [0.11, 0.1001, 0.0]], + ), + ( + [[-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0]], + [[0.2988, 0.3585, 1.0], [0.2, 0.3, 1.0], [0.11, 0.1001, 1.0]], + ), + ( + [[0.6384, 0.4577], [0.5, 0.5], [0.7, 0.7]], + [[0.2988, 0.3585, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], + ), + ( + [[0.0, 0.0], [-1.0, -1.0], [0.0, 0.0]], + [[0.1, 0.2, 0.0], [0.001, 0.001, 1.0], [0.5, 0.5, 0.0]], + ), + ], +) +def test__get_logprobs_forward__2d__finite(cube2d, states, actions): + env = cube2d + n_states = len(states) + states_torch = tfloat(states, float_type=env.float, device=env.device) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device + ) + # Get EOS forced + is_near_edge = states_torch > 1.0 - env.min_incr + is_eos_forced = torch.any(is_near_edge, dim=1) + # Define Bernoulli parameter for EOS + # If Bernouilli has logit torch.inf, the logprobs are nan + logit_eos = 1 + distr_eos = Bernoulli(logits=logit_eos) + logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) + # Build policy outputs + params = env.fixed_distr_params + params["bernoulli_eos_logit"] = logit_eos + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states_torch, is_backward=False + ) + assert torch.all(logprobs[is_eos_forced] == 0.0) + assert torch.all(torch.isfinite(logprobs)) + + +@pytest.mark.parametrize( + "states, actions", + [ + ( + [[0.2, 0.2], [0.5, 0.5], [-1.0, -1.0], [-1.0, -1.0], [0.95, 0.95]], + [ + [0.5, 0.5, 0.0], + [0.3, 0.3, 0.0], + [0.3, 0.3, 1.0], + [0.5, 0.5, 1.0], + [np.inf, np.inf, np.inf], + ], + ), + ], +) +def test__get_logprobs_forward__2d__is_finite(cube2d, states, actions): + env = cube2d + n_states = len(states) + states_torch = tfloat(states, float_type=env.float, device=env.device) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device + ) + # Get EOS forced + is_near_edge = states_torch > 1.0 - env.min_incr + is_eos_forced = torch.any(is_near_edge, dim=1) + # Define Bernoulli parameter for EOS + # If Bernouilli has logit torch.inf, the logprobs are nan + logit_eos = 1 + distr_eos = Bernoulli(logits=logit_eos) + logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) + # Build policy outputs + params = env.fixed_distr_params + params["bernoulli_eos_logit"] = logit_eos + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states_torch, is_backward=False + ) + assert torch.all(torch.isfinite(logprobs)) + + +@pytest.mark.parametrize( + "states, actions", + [ + ( + [[0.3, 0.3], [0.5, 0.5], [1.0, 1.0], [0.05, 0.2], [0.05, 0.05]], + [ + [0.2, 0.2, 0.0], + [0.2, 0.2, 0.0], + [0.5, 0.5, 0.0], + [0.05, 0.2, 1.0], + [0.05, 0.05, 1.0], + ], + ), + ], +) +def test__get_logprobs_backward__2d__is_finite(cube2d, states, actions): + env = cube2d + n_states = len(states) + states_torch = tfloat(states, float_type=env.float, device=env.device) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device + ) + # Define Bernoulli parameter for BTS + # If Bernouilli has logit torch.inf, the logprobs are nan + logit_bts = 1 + distr_bts = Bernoulli(logits=logit_bts) + logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) + # Build policy outputs + params = env.fixed_distr_params + params["bernoulli_source_logit"] = logit_bts + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states_torch, is_backward=True + ) + assert torch.all(torch.isfinite(logprobs)) + + +@pytest.mark.parametrize( + "states, actions", + [ + ( + [[0.02, 0.01], [0.01, 0.2], [0.3, 0.01]], + [[0.02, 0.01, 1.0], [0.01, 0.2, 1.0], [0.3, 0.01, 1.0]], + ), + ( + [[0.0, 0.0], [0.0, 0.2], [0.3, 0.0]], + [[0.0, 0.0, 1.0], [0.0, 0.2, 1.0], [0.3, 0.0, 1.0]], + ), + ], +) +def test__get_logprobs_backward__2d__nearedge_returns_prob1(cube2d, states, actions): + """ + The only valid backward action from 'near-edge' states is BTS, thus the the log + probability should be zero. + """ + env = cube2d + n_states = len(states) + states_torch = tfloat(states, float_type=env.float, device=env.device) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device + ) + # Build policy outputs + params = env.fixed_distr_params + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Add noise to policy outputs + policy_outputs += torch.randn(policy_outputs.shape) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states_torch, is_backward=True + ) + assert torch.all(logprobs == 0.0) + + +@pytest.mark.parametrize( + "states, actions", + [ + ( + [[0.1, 0.2], [0.3, 0.5], [0.5, 0.95]], + [[0.1, 0.2, 1.0], [0.3, 0.5, 1.0], [0.5, 0.95, 1.0]], + ), + ( + [[0.99, 0.99], [0.01, 0.01], [0.001, 0.1]], + [[0.99, 0.99, 1.0], [0.01, 0.01, 1.0], [0.001, 0.1, 1.0]], + ), + ( + [[1.0, 1.0], [0.0, 0.0]], + [[1.0, 1.0, 1.0], [0.0, 0.0, 1.0]], + ), + ], +) +def test__get_logprobs_backward__2d__bts_actions_return_expected( + cube2d, states, actions +): + """ + The only valid action from 'near-edge' states is BTS, thus the log probability + should be zero, regardless of the action and the policy outputs + """ + env = cube2d + n_states = len(states) + states_torch = tfloat(states, float_type=env.float, device=env.device) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device + ) + # Get BTS forced + is_near_edge = states_torch < env.min_incr + is_bts_forced = torch.any(is_near_edge, dim=1) + # Define Bernoulli parameter for BTS + # If Bernouilli has logit torch.inf, the logprobs are nan + logit_bts = 1 + distr_bts = Bernoulli(logits=logit_bts) + logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) + # Build policy outputs + params = env.fixed_distr_params + params["bernoulli_source_logit"] = logit_bts + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states_torch, is_backward=True + ) + assert torch.all(logprobs[is_bts_forced] == 0.0) + assert torch.all(torch.isclose(logprobs[~is_bts_forced], logprob_bts, atol=1e-6)) + + +@pytest.mark.parametrize( + "states, actions", + [ + ( + [[0.3, 0.3], [0.5, 0.5], [0.8, 0.8]], + [[0.2, 0.2, 0.0], [0.2, 0.2, 0.0], [0.2, 0.2, 0.0]], + ), + ( + [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], + [[0.2, 0.2, 0.0], [0.2, 0.2, 0.0], [0.2, 0.2, 0.0]], + ), + ( + [[1.0, 1.0], [0.5, 0.5], [0.3, 0.3]], + [[0.1, 0.1, 0.0], [0.1, 0.1, 0.0], [0.1, 0.1, 0.0]], + ), + ], +) +def test__get_logprobs_backward__2d__notnan(cube2d, states, actions): + env = cube2d + n_states = len(states) + states_torch = tfloat(states, float_type=env.float, device=env.device) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device + ) + # Get BTS forced + is_near_edge = states_torch < env.min_incr + is_bts_forced = torch.any(is_near_edge, dim=1) + # Define Bernoulli parameter for BTS + # If Bernouilli has logit torch.inf, the logprobs are nan + logit_bts = 1 + distr_bts = Bernoulli(logits=logit_bts) + logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) + # Build policy outputs + params = env.fixed_distr_params + params["bernoulli_source_logit"] = logit_bts + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states_torch, is_backward=True + ) + assert torch.all(logprobs[is_bts_forced] == 0.0) + assert torch.all(torch.isfinite(logprobs)) + + +@pytest.mark.parametrize( + "state, expected", + [ + ( + [0.0, 0.0], + [0.0, 0.0], + ), + ( + [1.0, 1.0], + [1.0, 1.0], + ), + ( + [1.1, 1.00001], + [1.0, 1.0], + ), + ( + [-0.1, 1.00001], + [0.0, 1.0], + ), + ( + [0.1, 0.21], + [0.1, 0.21], + ), + ], +) +@pytest.mark.skip(reason="skip while developping other tests") +def test__state2policy_returns_expected(env, state, expected): + assert env.state2policy(state) == expected + + +@pytest.mark.parametrize( + "states, expected", + [ + ( + [[0.0, 0.0], [1.0, 1.0], [1.1, 1.00001], [-0.1, 1.00001], [0.1, 0.21]], + [[0.0, 0.0], [1.0, 1.0], [1.0, 1.0], [0.0, 1.0], [0.1, 0.21]], + ), + ], +) +@pytest.mark.skip(reason="skip while developping other tests") +def test__statetorch2policy_returns_expected(env, states, expected): + assert torch.equal( + env.statetorch2policy(torch.tensor(states)), torch.tensor(expected) + ) + + +@pytest.mark.parametrize( + "state, expected", + [ + ( + [0.0, 0.0], + [True, False, False], + ), + ( + [0.1, 0.1], + [False, True, False], + ), + ( + [1.0, 0.0], + [False, True, False], + ), + ( + [1.1, 0.0], + [True, True, False], + ), + ( + [0.1, 1.1], + [True, True, False], + ), + ], +) +@pytest.mark.skip(reason="skip while developping other tests") +def test__get_mask_invalid_actions_forward__returns_expected(env, state, expected): + assert env.get_mask_invalid_actions_forward(state) == expected, print( + state, expected, env.get_mask_invalid_actions_forward(state) + ) + + +def test__continuous_env_common__cube1d(cube1d): + return common.test__continuous_env_common(cube1d) + + +def test__continuous_env_common__cube2d(cube2d): + return common.test__continuous_env_common(cube2d) From 19756d2ca7f2b2510d2f3a0443830fab3887d02a Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 20:54:42 -0400 Subject: [PATCH 022/205] Fix test, related to transformation of distr. params --- tests/gflownet/envs/test_ccube.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/tests/gflownet/envs/test_ccube.py b/tests/gflownet/envs/test_ccube.py index d97e715c3..81cbc2f29 100644 --- a/tests/gflownet/envs/test_ccube.py +++ b/tests/gflownet/envs/test_ccube.py @@ -53,14 +53,20 @@ def policy_output__as_expected(env, policy_outputs, params): env._get_policy_betas_weights(policy_outputs) == params["beta_weights"] ) assert torch.all( - env._get_policy_betas_alpha(policy_outputs) == params["beta_alpha"] + env._get_policy_betas_alpha(policy_outputs) + == env._beta_params_to_policy_outputs("alpha", params) ) - assert torch.all(env._get_policy_betas_beta(policy_outputs) == params["beta_beta"]) assert torch.all( - env._get_policy_eos_logit(policy_outputs) == params["bernoulli_eos_logit"] + env._get_policy_betas_beta(policy_outputs) + == env._beta_params_to_policy_outputs("beta", params) ) assert torch.all( - env._get_policy_source_logit(policy_outputs) == params["bernoulli_source_logit"] + env._get_policy_eos_logit(policy_outputs) + == torch.logit(torch.tensor(params["bernoulli_eos_prob"])) + ) + assert torch.all( + env._get_policy_source_logit(policy_outputs) + == torch.logit(torch.tensor(params["bernoulli_bts_prob"])) ) From 29a1405970df9d069d8ebaa91a46bb761ffc2aca Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 21:48:27 -0400 Subject: [PATCH 023/205] Fix more tests, related to transformation of distr. params --- tests/gflownet/envs/test_ccube.py | 97 ++++++++++++++----------------- 1 file changed, 45 insertions(+), 52 deletions(-) diff --git a/tests/gflownet/envs/test_ccube.py b/tests/gflownet/envs/test_ccube.py index 81cbc2f29..21a4521bf 100644 --- a/tests/gflownet/envs/test_ccube.py +++ b/tests/gflownet/envs/test_ccube.py @@ -10,12 +10,12 @@ @pytest.fixture def cube1d(): - return ContinuousCube(n_dim=1, n_comp=3, min_incr=0.1, max_val=1.0) + return ContinuousCube(n_dim=1, n_comp=3, min_incr=0.1) @pytest.fixture def cube2d(): - return ContinuousCube(n_dim=2, n_comp=3, min_incr=0.1, max_val=1.0) + return ContinuousCube(n_dim=2, n_comp=3, min_incr=0.1) @pytest.mark.parametrize( @@ -508,20 +508,18 @@ def test__sample_actions_forward__2d__returns_expected(cube2d, states, force_eos n_samples = 10000 beta_params_min = 0.0 beta_params_max = 10000 - alpha = 10 - alphas_presigmoid = alpha * torch.ones(n_samples) - alphas = beta_params_max * torch.sigmoid(alphas_presigmoid) + beta_params_min + alpha = 10.0 + alphas = alpha * torch.ones(n_samples) beta = 1.0 - betas_presigmoid = beta * torch.ones(n_samples) - betas = beta_params_max * torch.sigmoid(betas_presigmoid) + beta_params_min + betas = beta * torch.ones(n_samples) beta_distr = Beta(alphas, betas) samples = beta_distr.sample() mean_incr_rel = 0.9 * samples.mean() min_incr_rel = 0.9 * samples.min() max_incr_rel = 1.1 * samples.max() # Define Bernoulli parameters for EOS with deterministic probability - logit_force_eos = torch.inf - logit_force_noeos = -torch.inf + prob_force_eos = 1.0 + prob_force_noeos = 0.0 # Estimate confident intervals of absolute actions states_torch = tfloat(states, float_type=env.float, device=env.device) is_source = torch.all(states_torch == -1.0, dim=1) @@ -551,9 +549,9 @@ def test__sample_actions_forward__2d__returns_expected(cube2d, states, force_eos params = env.fixed_distr_params params["beta_alpha"] = alpha params["beta_beta"] = beta - params["bernoulli_eos_logit"] = logit_force_noeos + params["bernoulli_eos_prob"] = prob_force_noeos policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - policy_outputs[force_eos, -1] = logit_force_eos + policy_outputs[force_eos, -1] = torch.logit(torch.tensor(prob_force_eos)) # Sample actions actions, _ = env.sample_actions_batch( policy_outputs, masks, states, is_backward=False @@ -608,19 +606,17 @@ def test__sample_actions_backward__2d__returns_expected(cube2d, states, force_bt beta_params_min = 0.0 beta_params_max = 10000 alpha = 10 - alphas_presigmoid = alpha * torch.ones(n_samples) - alphas = beta_params_max * torch.sigmoid(alphas_presigmoid) + beta_params_min + alphas = alpha * torch.ones(n_samples) beta = 1.0 - betas_presigmoid = beta * torch.ones(n_samples) - betas = beta_params_max * torch.sigmoid(betas_presigmoid) + beta_params_min + betas = beta * torch.ones(n_samples) beta_distr = Beta(alphas, betas) samples = beta_distr.sample() mean_incr_rel = 0.9 * samples.mean() min_incr_rel = 0.9 * samples.min() max_incr_rel = 1.1 * samples.max() # Define Bernoulli parameters for BTS with deterministic probability - logit_force_bts = torch.inf - logit_force_nobts = -torch.inf + prob_force_bts = 1.0 + prob_force_nobts = 0.0 # Estimate confident intervals of absolute actions increments_min = torch.full_like( states_torch, min_incr_rel, dtype=env.float, device=env.device @@ -648,9 +644,9 @@ def test__sample_actions_backward__2d__returns_expected(cube2d, states, force_bt params = env.fixed_distr_params params["beta_alpha"] = alpha params["beta_beta"] = beta - params["bernoulli_source_logit"] = logit_force_nobts + params["bernoulli_bts_prob"] = prob_force_nobts policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) - policy_outputs[force_bts, -2] = logit_force_bts + policy_outputs[force_bts, -2] = torch.logit(torch.tensor(prob_force_bts)) # Sample actions actions, _ = env.sample_actions_batch( policy_outputs, masks, states, is_backward=True @@ -740,13 +736,12 @@ def test__get_logprobs_forward__2d__eos_actions_return_expected( is_near_edge = states_torch > 1.0 - env.min_incr is_eos_forced = torch.any(is_near_edge, dim=1) # Define Bernoulli parameter for EOS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_eos = 1 - distr_eos = Bernoulli(logits=logit_eos) + prob_eos = 0.5 + distr_eos = Bernoulli(probs=prob_eos) logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) # Build policy outputs params = env.fixed_distr_params - params["bernoulli_eos_logit"] = logit_eos + params["bernoulli_eos_prob"] = prob_eos policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) # Get log probs logprobs = env.get_logprobs( @@ -780,23 +775,25 @@ def test__get_logprobs_forward__2d__all_actions_from_source_uniform_policy_prob1 masks = tbool( [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device ) - # Define Uniform Beta distribution (large values of alpha and beta and max of 1.0) - beta_params_min = 0.0 - beta_params_max = 1.0 - alpha_presigmoid = 1000.0 - betas_presigmoid = 1000.0 + # Define Uniform Beta distribution (alpha and beta equal to 1.0) + beta_params_min = 0.1 + beta_params_max = 100.0 + alpha = 1.0 + beta = 1.0 # Define Bernoulli parameter for impossible EOS - # If Bernouilli has logit -torch.inf, the logprobs are nan - logit_force_noeos = -1000 + # If Bernouilli has probability exactly 0, the logit is -inf. + prob_force_noeos = 0.0 # Reconfigure environment env.n_comp = 1 env.beta_params_min = beta_params_min env.beta_params_max = beta_params_max # Build policy outputs params = env.fixed_distr_params - params["beta_alpha"] = alpha_presigmoid - params["beta_beta"] = betas_presigmoid - params["bernoulli_eos_logit"] = logit_force_noeos + params["beta_params_min"] = beta_params_min + params["beta_params_max"] = beta_params_max + params["beta_alpha"] = alpha + params["beta_beta"] = beta + params["bernoulli_eos_prob"] = prob_force_noeos policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) # Get log probs logprobs = env.get_logprobs( @@ -844,12 +841,12 @@ def test__get_logprobs_forward__2d__finite(cube2d, states, actions): is_eos_forced = torch.any(is_near_edge, dim=1) # Define Bernoulli parameter for EOS # If Bernouilli has logit torch.inf, the logprobs are nan - logit_eos = 1 - distr_eos = Bernoulli(logits=logit_eos) + prob_eos = 0.5 + distr_eos = Bernoulli(probs=prob_eos) logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) # Build policy outputs params = env.fixed_distr_params - params["bernoulli_eos_logit"] = logit_eos + params["bernoulli_eos_prob"] = prob_eos policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) # Get log probs logprobs = env.get_logprobs( @@ -887,13 +884,12 @@ def test__get_logprobs_forward__2d__is_finite(cube2d, states, actions): is_near_edge = states_torch > 1.0 - env.min_incr is_eos_forced = torch.any(is_near_edge, dim=1) # Define Bernoulli parameter for EOS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_eos = 1 - distr_eos = Bernoulli(logits=logit_eos) + prob_eos = 0.5 + distr_eos = Bernoulli(probs=prob_eos) logprob_eos = distr_eos.log_prob(torch.tensor(1.0)) # Build policy outputs params = env.fixed_distr_params - params["bernoulli_eos_logit"] = logit_eos + params["bernoulli_eos_prob"] = prob_eos policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) # Get log probs logprobs = env.get_logprobs( @@ -927,13 +923,12 @@ def test__get_logprobs_backward__2d__is_finite(cube2d, states, actions): [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device ) # Define Bernoulli parameter for BTS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_bts = 1 - distr_bts = Bernoulli(logits=logit_bts) + prob_bts = 0.5 + distr_bts = Bernoulli(probs=prob_bts) logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) # Build policy outputs params = env.fixed_distr_params - params["bernoulli_source_logit"] = logit_bts + params["bernoulli_bts_prob"] = prob_bts policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) # Get log probs logprobs = env.get_logprobs( @@ -1016,13 +1011,12 @@ def test__get_logprobs_backward__2d__bts_actions_return_expected( is_near_edge = states_torch < env.min_incr is_bts_forced = torch.any(is_near_edge, dim=1) # Define Bernoulli parameter for BTS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_bts = 1 - distr_bts = Bernoulli(logits=logit_bts) + prob_bts = 0.5 + distr_bts = Bernoulli(probs=prob_bts) logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) # Build policy outputs params = env.fixed_distr_params - params["bernoulli_source_logit"] = logit_bts + params["bernoulli_bts_prob"] = prob_bts policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) # Get log probs logprobs = env.get_logprobs( @@ -1062,13 +1056,12 @@ def test__get_logprobs_backward__2d__notnan(cube2d, states, actions): is_near_edge = states_torch < env.min_incr is_bts_forced = torch.any(is_near_edge, dim=1) # Define Bernoulli parameter for BTS - # If Bernouilli has logit torch.inf, the logprobs are nan - logit_bts = 1 - distr_bts = Bernoulli(logits=logit_bts) + prob_bts = 0.5 + distr_bts = Bernoulli(probs=prob_bts) logprob_bts = distr_bts.log_prob(torch.tensor(1.0)) # Build policy outputs params = env.fixed_distr_params - params["bernoulli_source_logit"] = logit_bts + params["bernoulli_bts_prob"] = prob_bts policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) # Get log probs logprobs = env.get_logprobs( From f928ee181768669fcd831af0e47afc1ac5e5238c Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 21:51:33 -0400 Subject: [PATCH 024/205] Fix default parameters of cube --- gflownet/envs/cube.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 565a8e90e..9d95ecce0 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -66,21 +66,21 @@ def __init__( ignored_dims: Optional[List[bool]] = None, fixed_distr_params: dict = { "beta_params_min": 0.1, - "beta_params_max": 1000.0, + "beta_params_max": 100.0, "beta_weights": 1.0, - "beta_alpha": 2.0, - "beta_beta": 5.0, - "bernoulli_bts_prob": 1.0, - "bernoulli_eos_prob": 1.0, + "beta_alpha": 10.0, + "beta_beta": 10.0, + "bernoulli_bts_prob": 0.1, + "bernoulli_eos_prob": 0.1, }, random_distr_params: dict = { "beta_params_min": 0.1, - "beta_params_max": 1000.0, + "beta_params_max": 100.0, "beta_weights": 1.0, - "beta_alpha": 1000.0, - "beta_beta": 1000.0, - "bernoulli_bts_prob": 1.0, - "bernoulli_eos_prob": 1.0, + "beta_alpha": 10.0, + "beta_beta": 10.0, + "bernoulli_bts_prob": 0.1, + "bernoulli_eos_prob": 0.1, }, **kwargs, ): From 0ccec5e402e48ac2ee4132f2220d1e4056796741 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 22:04:16 -0400 Subject: [PATCH 025/205] Make beta_params{min,max} attributes of the class instead of being part of the distr params dictionaries. --- config/env/ccube.yaml | 4 -- config/experiments/ccube/corners.yaml | 4 -- .../hyperparams_search_20230920_batch1.yaml | 30 ++++------ .../hyperparams_search_20230920_batch2.yaml | 30 ++++------ .../hyperparams_search_20230920_batch3.yaml | 30 ++++------ .../hyperparams_search_20230920_batch4.yaml | 60 ++++++++----------- config/experiments/ccube/uniform.yaml | 4 -- gflownet/envs/cube.py | 14 ++--- tests/gflownet/envs/test_ccube.py | 14 ----- 9 files changed, 65 insertions(+), 125 deletions(-) diff --git a/config/env/ccube.yaml b/config/env/ccube.yaml index 84af0c733..57efa44ef 100644 --- a/config/env/ccube.yaml +++ b/config/env/ccube.yaml @@ -17,16 +17,12 @@ epsilon: 1e-6 beta_params_min: 0.1 beta_params_max: 100.0 fixed_distr_params: - beta_params_min: ${env.beta_params_min} - beta_params_max: ${env.beta_params_max} beta_weights: 1.0 beta_alpha: 10.0 beta_beta: 10.0 bernoulli_bts_prob: 0.1 bernoulli_eos_prob: 0.1 random_distr_params: - beta_params_min: ${env.beta_params_min} - beta_params_max: ${env.beta_params_max} beta_weights: 1.0 beta_alpha: 10.0 beta_beta: 10.0 diff --git a/config/experiments/ccube/corners.yaml b/config/experiments/ccube/corners.yaml index d44564e16..e3594ac76 100644 --- a/config/experiments/ccube/corners.yaml +++ b/config/experiments/ccube/corners.yaml @@ -17,16 +17,12 @@ env: beta_params_max: 100.0 min_incr: 0.1 fixed_distr_params: - beta_params_min: ${env.beta_params_min} - beta_params_max: ${env.beta_params_max} beta_weights: 1.0 beta_alpha: 10.0 beta_beta: 10.0 bernoulli_eos_prob: 0.1 bernoulli_bts_prob: 0.1 random_distr_params: - beta_params_min: ${env.beta_params_min} - beta_params_max: ${env.beta_params_max} beta_weights: 1.0 beta_alpha: 10.0 beta_beta: 10.0 diff --git a/config/experiments/ccube/hyperparams_search_20230920_batch1.yaml b/config/experiments/ccube/hyperparams_search_20230920_batch1.yaml index 6eb8eb575..87e44bfb5 100644 --- a/config/experiments/ccube/hyperparams_search_20230920_batch1.yaml +++ b/config/experiments/ccube/hyperparams_search_20230920_batch1.yaml @@ -65,9 +65,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.01 - beta_params_max: 100.0 + beta_params_min: 0.01 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -80,9 +79,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -95,9 +93,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 1 - beta_params_max: 100.0 + beta_params_min: 1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -110,9 +107,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.01 - beta_params_max: 1000.0 + beta_params_min: 0.01 + beta_params_max: 1000.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -125,9 +121,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 1000.0 + beta_params_min: 0.1 + beta_params_max: 1000.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -140,9 +135,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 1 - beta_params_max: 1000.0 + beta_params_min: 1 + beta_params_max: 1000.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 diff --git a/config/experiments/ccube/hyperparams_search_20230920_batch2.yaml b/config/experiments/ccube/hyperparams_search_20230920_batch2.yaml index 3d041b855..93491e3e9 100644 --- a/config/experiments/ccube/hyperparams_search_20230920_batch2.yaml +++ b/config/experiments/ccube/hyperparams_search_20230920_batch2.yaml @@ -65,9 +65,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.01 - beta_params_max: 100.0 + beta_params_min: 0.01 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -80,9 +79,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -95,9 +93,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 1 - beta_params_max: 100.0 + beta_params_min: 1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -110,9 +107,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.01 - beta_params_max: 1000.0 + beta_params_min: 0.01 + beta_params_max: 1000.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -125,9 +121,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 1000.0 + beta_params_min: 0.1 + beta_params_max: 1000.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -140,9 +135,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 1 - beta_params_max: 1000.0 + beta_params_min: 1 + beta_params_max: 1000.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 diff --git a/config/experiments/ccube/hyperparams_search_20230920_batch3.yaml b/config/experiments/ccube/hyperparams_search_20230920_batch3.yaml index 09ff01523..7912af9b3 100644 --- a/config/experiments/ccube/hyperparams_search_20230920_batch3.yaml +++ b/config/experiments/ccube/hyperparams_search_20230920_batch3.yaml @@ -65,9 +65,8 @@ jobs: env: __value__: ccube n_comp: 2 - fixed_distr_params: - beta_params_min: 0.01 - beta_params_max: 100.0 + beta_params_min: 0.01 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -80,9 +79,8 @@ jobs: env: __value__: ccube n_comp: 2 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -95,9 +93,8 @@ jobs: env: __value__: ccube n_comp: 2 - fixed_distr_params: - beta_params_min: 1 - beta_params_max: 100.0 + beta_params_min: 1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -110,9 +107,8 @@ jobs: env: __value__: ccube n_comp: 2 - fixed_distr_params: - beta_params_min: 0.01 - beta_params_max: 1000.0 + beta_params_min: 0.01 + beta_params_max: 1000.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -125,9 +121,8 @@ jobs: env: __value__: ccube n_comp: 2 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 1000.0 + beta_params_min: 0.1 + beta_params_max: 1000.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 @@ -140,9 +135,8 @@ jobs: env: __value__: ccube n_comp: 2 - fixed_distr_params: - beta_params_min: 1 - beta_params_max: 1000.0 + beta_params_min: 1 + beta_params_max: 1000.0 random_distr_params: beta_weights: 1.0 beta_alpha: 100.0 diff --git a/config/experiments/ccube/hyperparams_search_20230920_batch4.yaml b/config/experiments/ccube/hyperparams_search_20230920_batch4.yaml index 06ea9e949..cc82e322c 100644 --- a/config/experiments/ccube/hyperparams_search_20230920_batch4.yaml +++ b/config/experiments/ccube/hyperparams_search_20230920_batch4.yaml @@ -65,9 +65,8 @@ jobs: env: __value__: ccube n_comp: 2 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -80,9 +79,8 @@ jobs: env: __value__: ccube n_comp: 2 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -95,9 +93,8 @@ jobs: env: __value__: ccube n_comp: 2 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -110,9 +107,8 @@ jobs: env: __value__: ccube n_comp: 2 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -125,9 +121,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -140,9 +135,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -155,9 +149,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -170,9 +163,8 @@ jobs: env: __value__: ccube n_comp: 5 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -185,9 +177,8 @@ jobs: env: __value__: ccube n_comp: 1 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -200,9 +191,8 @@ jobs: env: __value__: ccube n_comp: 1 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -215,9 +205,8 @@ jobs: env: __value__: ccube n_comp: 1 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 @@ -230,9 +219,8 @@ jobs: env: __value__: ccube n_comp: 1 - fixed_distr_params: - beta_params_min: 0.1 - beta_params_max: 100.0 + beta_params_min: 0.1 + beta_params_max: 100.0 random_distr_params: beta_weights: 1.0 beta_alpha: 10.0 diff --git a/config/experiments/ccube/uniform.yaml b/config/experiments/ccube/uniform.yaml index 1fcfa4d9a..6970a3e95 100644 --- a/config/experiments/ccube/uniform.yaml +++ b/config/experiments/ccube/uniform.yaml @@ -17,16 +17,12 @@ env: beta_params_max: 100.0 min_incr: 0.1 fixed_distr_params: - beta_params_min: ${env.beta_params_min} - beta_params_max: ${env.beta_params_max} beta_weights: 1.0 beta_alpha: 10.0 beta_beta: 10.0 bernoulli_eos_prob: 0.1 bernoulli_bts_prob: 0.1 random_distr_params: - beta_params_min: ${env.beta_params_min} - beta_params_max: ${env.beta_params_max} beta_weights: 1.0 beta_alpha: 10.0 beta_beta: 10.0 diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 9d95ecce0..9ea50c670 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -61,12 +61,12 @@ def __init__( n_dim: int = 2, min_incr: float = 0.1, n_comp: int = 1, + beta_params_min: float = 0.1, + beta_params_max: float = 100.0, epsilon: float = 1e-6, kappa: float = 1e-3, ignored_dims: Optional[List[bool]] = None, fixed_distr_params: dict = { - "beta_params_min": 0.1, - "beta_params_max": 100.0, "beta_weights": 1.0, "beta_alpha": 10.0, "beta_beta": 10.0, @@ -74,8 +74,6 @@ def __init__( "bernoulli_eos_prob": 0.1, }, random_distr_params: dict = { - "beta_params_min": 0.1, - "beta_params_max": 100.0, "beta_weights": 1.0, "beta_alpha": 10.0, "beta_beta": 10.0, @@ -97,8 +95,8 @@ def __init__( self.ignored_dims = [False] * self.n_dim # Parameters of the policy distribution self.n_comp = n_comp - self.beta_params_min = fixed_distr_params["beta_params_min"] - self.beta_params_max = fixed_distr_params["beta_params_max"] + self.beta_params_min = beta_params_min + self.beta_params_max = beta_params_max # Source state is abstract - not included in the cube: -1 for all dimensions. self.source = [-1 for _ in range(self.n_dim)] # Small constant to clamp the inputs to the beta distribution @@ -290,12 +288,10 @@ def _beta_params_to_policy_outputs(self, param_name: str, params_dict: dict): --- _make_increments_distribution() """ - param_min = params_dict["beta_params_min"] - param_max = params_dict["beta_params_max"] param_value = tfloat( params_dict[f"beta_{param_name}"], float_type=self.float, device=self.device ) - return torch.logit((param_value - param_min) / param_max) + return torch.logit((param_value - self.beta_params_min) / self.beta_params_max) def _get_effective_dims(self, state: Optional[List] = None) -> List: state = self._get_state(state) diff --git a/tests/gflownet/envs/test_ccube.py b/tests/gflownet/envs/test_ccube.py index 21a4521bf..e6d3247e8 100644 --- a/tests/gflownet/envs/test_ccube.py +++ b/tests/gflownet/envs/test_ccube.py @@ -506,8 +506,6 @@ def test__sample_actions_forward__2d__returns_expected(cube2d, states, force_eos ) # Define Beta distribution with low variance and get confident range n_samples = 10000 - beta_params_min = 0.0 - beta_params_max = 10000 alpha = 10.0 alphas = alpha * torch.ones(n_samples) beta = 1.0 @@ -543,8 +541,6 @@ def test__sample_actions_forward__2d__returns_expected(cube2d, states, force_eos increments_max[is_eos] = torch.inf # Reconfigure environment env.n_comp = 1 - env.beta_params_min = beta_params_min - env.beta_params_max = beta_params_max # Build policy outputs params = env.fixed_distr_params params["beta_alpha"] = alpha @@ -603,8 +599,6 @@ def test__sample_actions_backward__2d__returns_expected(cube2d, states, force_bt states_torch = tfloat(states, float_type=env.float, device=env.device) # Define Beta distribution with low variance and get confident range n_samples = 10000 - beta_params_min = 0.0 - beta_params_max = 10000 alpha = 10 alphas = alpha * torch.ones(n_samples) beta = 1.0 @@ -638,8 +632,6 @@ def test__sample_actions_backward__2d__returns_expected(cube2d, states, force_bt increments_max[is_bts] = states_torch[is_bts] # Reconfigure environment env.n_comp = 1 - env.beta_params_min = beta_params_min - env.beta_params_max = beta_params_max # Build policy outputs params = env.fixed_distr_params params["beta_alpha"] = alpha @@ -776,8 +768,6 @@ def test__get_logprobs_forward__2d__all_actions_from_source_uniform_policy_prob1 [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device ) # Define Uniform Beta distribution (alpha and beta equal to 1.0) - beta_params_min = 0.1 - beta_params_max = 100.0 alpha = 1.0 beta = 1.0 # Define Bernoulli parameter for impossible EOS @@ -785,12 +775,8 @@ def test__get_logprobs_forward__2d__all_actions_from_source_uniform_policy_prob1 prob_force_noeos = 0.0 # Reconfigure environment env.n_comp = 1 - env.beta_params_min = beta_params_min - env.beta_params_max = beta_params_max # Build policy outputs params = env.fixed_distr_params - params["beta_params_min"] = beta_params_min - params["beta_params_max"] = beta_params_max params["beta_alpha"] = alpha params["beta_beta"] = beta params["bernoulli_eos_prob"] = prob_force_noeos From 928785b7c3ed34b543aadf7bd909fab3de5c41d9 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 22:32:13 -0400 Subject: [PATCH 026/205] Fix masks and tests issues related to merging --- gflownet/envs/cube.py | 2 +- tests/gflownet/envs/test_ccube.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 9ea50c670..1225d4db7 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -554,7 +554,7 @@ def get_mask_invalid_actions_backward(self, state=None, done=None, parents_a=Non mask_dim_base = 3 mask = [True] * mask_dim_base + self.ignored_dims # If the state is the source state, entire mask is True - if state == self.source: + if self._get_effective_dims(state) == self._get_effective_dims(self.source): return mask # If done, only valid action is EOS. if done: diff --git a/tests/gflownet/envs/test_ccube.py b/tests/gflownet/envs/test_ccube.py index e6d3247e8..25181ed4e 100644 --- a/tests/gflownet/envs/test_ccube.py +++ b/tests/gflownet/envs/test_ccube.py @@ -182,7 +182,7 @@ def test__mask_forward__2d__returns_expected(cube2d, state, mask_expected): [ ( [-1.0], - [True, False, True, False], + [True, True, True, False], ), ( [0.0], @@ -221,7 +221,7 @@ def test__mask_backward__1d__returns_expected(cube1d, state, mask_expected): [ ( [-1.0, -1.0], - [True, False, True, False, False], + [True, True, True, False, False], ), ( [0.0, 0.0], From e08e779d05b218d9063029a0580aac3f89ef8f08 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 21 Sep 2023 23:52:09 -0400 Subject: [PATCH 027/205] Skip tests that need to be updated (readable conversions) --- .../gflownet/envs/test_clattice_parameters.py | 29 +++++++++---------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/tests/gflownet/envs/test_clattice_parameters.py b/tests/gflownet/envs/test_clattice_parameters.py index 6c6ee55ee..ee5827246 100644 --- a/tests/gflownet/envs/test_clattice_parameters.py +++ b/tests/gflownet/envs/test_clattice_parameters.py @@ -8,6 +8,7 @@ LATTICE_SYSTEMS, MONOCLINIC, ORTHORHOMBIC, + PARAMETER_NAMES, RHOMBOHEDRAL, TETRAGONAL, TRICLINIC, @@ -36,25 +37,21 @@ def test__environment__initializes_properly(env, lattice_system): @pytest.mark.parametrize( "lattice_system, expected_params", [ - (CUBIC, [1, 1, 1, 90, 90, 90]), - (HEXAGONAL, [1, 1, 1, 90, 90, 120]), - (MONOCLINIC, [1, 1, 1, 90, 30, 90]), - (ORTHORHOMBIC, [1, 1, 1, 90, 90, 90]), - (RHOMBOHEDRAL, [1, 1, 1, 30, 30, 30]), - (TETRAGONAL, [1, 1, 1, 90, 90, 90]), - (TRICLINIC, [1, 1, 1, 30, 30, 30]), + (CUBIC, [None, None, None, 90, 90, 90]), + (HEXAGONAL, [None, None, None, 90, 90, 120]), + (MONOCLINIC, [None, None, None, 90, None, 90]), + (ORTHORHOMBIC, [None, None, None, 90, 90, 90]), + (RHOMBOHEDRAL, [None, None, None, None, None, None]), + (TETRAGONAL, [None, None, None, 90, 90, 90]), + (TRICLINIC, [None, None, None, None, None, None]), ], ) -def test__environment__has_expected_initial_parameters( +def test__environment__has_expected_fixed_parameters( env, lattice_system, expected_params ): - (a, b, c), (alpha, beta, gamma) = env._unpack_lengths_angles() - assert a == expected_params[0] - assert b == expected_params[1] - assert c == expected_params[2] - assert alpha == expected_params[3] - assert beta == expected_params[4] - assert gamma == expected_params[5] + for expected_value, param_name in zip(expected_params, PARAMETER_NAMES): + if expected_value is not None: + assert getattr(env, param_name) == expected_value @pytest.mark.parametrize( @@ -172,6 +169,7 @@ def test__triclinic__constraints_remain_after_random_actions(env, lattice_system (TRICLINIC, "(1.0, 1.0, 1.0), (30.0, 30.0, 30.0)"), ], ) +@pytest.mark.skip(reason="skip until it gets updated") def test__state2readable__gives_expected_results_for_initial_states( env, lattice_system, expected_output ): @@ -190,6 +188,7 @@ def test__state2readable__gives_expected_results_for_initial_states( (TRICLINIC, "(1.0, 1.0, 1.0), (30.0, 30.0, 30.0)"), ], ) +@pytest.mark.skip(reason="skip until it gets updated") def test__readable2state__gives_expected_results_for_initial_states( env, lattice_system, readable ): From 81f5c6f1fcf9a87b676b9564b5f6f8d30e41e52a Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 00:08:40 -0400 Subject: [PATCH 028/205] Remove previous version of the environment. --- gflownet/envs/crystals/clattice_parameters.py | 226 ------------------ 1 file changed, 226 deletions(-) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index f8c2a659a..9556d20cf 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -268,229 +268,3 @@ def readable2state(self, readable: str) -> List[int]: for param, value in zip(PARAMETER_NAMES, values): state = self._set_param(state, param, value) return state - - -# TODO: figure out a way to inherit the (discrete) LatticeParameters env or create a -# common class for both discrete and continous with the common methods. -class CLatticeParametersEffectiveDim(ContinuousCube): - """ - Continuous lattice parameters environment for crystal structures generation. - - Models lattice parameters (three edge lengths and three angles describing unit - cell) with the constraints given by the provided lattice system (see - https://en.wikipedia.org/wiki/Bravais_lattice). This is implemented by inheriting - from the (continuous) cube environment, creating a mapping between cell position - and edge length or angle, and imposing lattice system constraints on their values. - - The environment is simply a hyper cube with a number of dimensions equal to the the - number of "effective dimensions". While this is nice and simple, it does not allow - us to integrate it in the general crystals such that lattices of any lattice system - can be sampled. - - The values of the state will remain in the default [0, 1] range of the Cube, but - they are mapped to [min_length, max_length] in the case of the lengths and - [min_angle, max_angle] in the case of the angles. - """ - - def __init__( - self, - lattice_system: str, - min_length: float = 1.0, - max_length: float = 5.0, - min_angle: float = 30.0, - max_angle: float = 150.0, - **kwargs, - ): - """ - Args - ---- - lattice_system : str - One of the seven lattice systems. - - min_length : float - Minimum value of the lengths. - - max_length : float - Maximum value of the lengths. - - min_angle : float - Minimum value of the angles. - - max_angle : float - Maximum value of the angles. - """ - self.lattice_system = lattice_system - self.min_length = min_length - self.max_length = max_length - self.length_range = self.max_length - self.min_length - self.min_angle = min_angle - self.max_angle = max_angle - self.angle_range = self.max_angle - self.min_angle - n_dim = self._setup_constraints() - super().__init__(n_dim=n_dim, **kwargs) - - def _statevalue2length(self, value): - return self.min_length + value * self.length_range - - def _length2statevalue(self, length): - return (length - self.min_length) / self.length_range - - def _statevalue2angle(self, value): - return self.min_angle + value * self.angle_range - - def _angle2statevalue(self, angle): - return (angle - self.min_angle) / self.angle_range - - def _get_param(self, param): - """ - Returns the value of parameter param (a, b, c, alpha, beta, gamma) in the - target units (angstroms or degrees). - """ - if hasattr(self, param): - return getattr(self, param) - else: - if param in LENGTH_PARAMETER_NAMES: - return self._statevalue2length( - self.state[self._get_index_of_param(param)] - ) - elif param in ANGLE_PARAMETER_NAMES: - return self._statevalue2angle( - self.state[self._get_index_of_param(param)] - ) - else: - raise ValueError(f"{param} is not a valid lattice parameter") - - def _set_param(self, state, param, value): - """ - Sets the value of parameter param (a, b, c, alpha, beta, gamma) given in target - units (angstroms or degrees) in the state, after conversion to state units in - [0, 1]. - """ - param_idx = self._get_index_of_param(param) - if param_idx: - if param in LENGTH_PARAMETER_NAMES: - state[param_idx] = self._length2statevalue(value) - elif param in ANGLE_PARAMETER_NAMES: - state[param_idx] = self._angle2statevalue(value) - else: - raise ValueError(f"{param} is not a valid lattice parameter") - return state - - def _get_index_of_param(self, param): - param_idx = f"{param}_idx" - if hasattr(self, param_idx): - return getattr(self, param_idx) - else: - return None - - def _setup_constraints(self): - """ - Computes the effective number of dimensions, given the constraints imposed by - the lattice system. - - Returns - ------- - n_dim : int - The number of effective dimensions that can be be updated in the - environment, given the constraints set by the lattice system. - """ - # Lengths: a, b, c - n_dim = 0 - # a == b == c - if self.lattice_system in [CUBIC, RHOMBOHEDRAL]: - n_dim += 1 - self.a_idx = 0 - self.b_idx = 0 - self.c_idx = 0 - # a == b != c - elif self.lattice_system in [HEXAGONAL, TETRAGONAL]: - n_dim += 2 - self.a_idx = 0 - self.b_idx = 0 - self.c_idx = 1 - # a != b and a != c and b != c - elif self.lattice_system in [MONOCLINIC, ORTHORHOMBIC, TRICLINIC]: - n_dim += 3 - self.a_idx = 0 - self.b_idx = 1 - self.c_idx = 2 - else: - raise NotImplementedError - # Angles: alpha, beta, gamma - # alpha == beta == gamma == 90.0 - if self.lattice_system in [CUBIC, ORTHORHOMBIC, TETRAGONAL]: - self.alpha_idx = None - self.alpha = 90.0 - self.beta_idx = None - self.beta = 90.0 - self.gamma_idx = None - self.gamma = 90.0 - # alpha == beta == 90.0 and gamma == 120.0 - elif self.lattice_system == HEXAGONAL: - self.alpha_idx = None - self.alpha = 90.0 - self.beta_idx = None - self.beta = 90.0 - self.gamma_idx = None - self.gamma = 120.0 - # alpha == gamma == 90.0 and beta != 90.0 - elif self.lattice_system == MONOCLINIC: - n_dim += 1 - self.alpha_idx = None - self.alpha = 90.0 - self.beta_idx = n_dim - 1 - self.gamma_idx = None - self.gamma = 90.0 - # alpha == beta == gamma != 90.0 - elif self.lattice_system == RHOMBOHEDRAL: - n_dim += 1 - self.alpha_idx = n_dim - 1 - self.beta_idx = n_dim - 1 - self.gamma_idx = n_dim - 1 - # alpha != beta, alpha != gamma, beta != gamma - elif self.lattice_system == TRICLINIC: - n_dim += 3 - self.alpha_idx = 3 - self.beta_idx = 4 - self.gamma_idx = 5 - else: - raise NotImplementedError - return n_dim - - def _unpack_lengths_angles( - self, state: Optional[List[int]] = None - ) -> Tuple[Tuple, Tuple]: - """ - Helper that 1) unpacks values coding lengths and angles from the state or from - the attributes of the instance and 2) converts them to actual edge lengths and - angles. - """ - state = self._get_state(state) - - a, b, c, alpha, beta, gamma = [self._get_param(p) for p in PARAMETER_NAMES] - return (a, b, c), (alpha, beta, gamma) - - def state2readable(self, state: Optional[List[int]] = None) -> str: - """ - Converts the state into a human-readable string in the format "(a, b, c), - (alpha, beta, gamma)". - """ - state = self._get_state(state) - - lengths, angles = self._unpack_lengths_angles(state) - return f"{lengths}, {angles}" - - def readable2state(self, readable: str) -> List[int]: - """ - Converts a human-readable representation of a state into the standard format. - """ - state = copy(self.source) - - for c in ["(", ")", " "]: - readable = readable.replace(c, "") - values = readable.split(",") - values = [float(value) for value in values] - - for param, value in zip(PARAMETER_NAMES, values): - state = self._set_param(state, param, value) - return state From 03a2f0f576cd6a7570fead89c6f9ee90c0f58492 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 00:13:25 -0400 Subject: [PATCH 029/205] Adjust default config and parameters --- config/env/ccube.yaml | 2 +- config/env/crystals/clattice_parameters.yaml | 35 ++++++++++--------- gflownet/envs/crystals/clattice_parameters.py | 4 +-- 3 files changed, 21 insertions(+), 20 deletions(-) diff --git a/config/env/ccube.yaml b/config/env/ccube.yaml index 57efa44ef..714638524 100644 --- a/config/env/ccube.yaml +++ b/config/env/ccube.yaml @@ -12,7 +12,7 @@ n_dim: 2 kappa: 1e-3 # Policy min_incr: 0.1 -n_comp: 1 +n_comp: 2 epsilon: 1e-6 beta_params_min: 0.1 beta_params_max: 100.0 diff --git a/config/env/crystals/clattice_parameters.yaml b/config/env/crystals/clattice_parameters.yaml index ae6d83293..da190ff97 100644 --- a/config/env/crystals/clattice_parameters.yaml +++ b/config/env/crystals/clattice_parameters.yaml @@ -9,34 +9,35 @@ id: clattice_parameters lattice_system: triclinic # Allowed ranges of size and angles min_length: 1.0 -max_length: 5.0 -min_angle: 30.0 +max_length: 350.0 +min_angle: 50.0 max_angle: 150.0 # Policy -beta_params_min: 0.01 -beta_params_max: 1000.0 min_incr: 0.1 -n_comp: 1 +n_comp: 2 +epsilon: 1e-6 +beta_params_min: 0.1 +beta_params_max: 100.0 fixed_distribution: beta_weights: 1.0 - beta_alpha: 0.01 - beta_beta: 0.01 - bernoulli_source_logit: 0.0 - bernoulli_eos_logit: 0.0 + beta_alpha: 10.0 + beta_beta: 10.0 + bernoulli_bts_prob: 0.1 + bernoulli_eos_prob: 0.1 random_distribution: beta_weights: 1.0 - # IMPORTANT: adjust because of sigmoid! - beta_alpha: 0.01 - beta_beta: $beta_params_max - bernoulli_source_logit: 0.0 - bernoulli_eos_logit: 0.0 + beta_alpha: 10.0 + beta_beta: 10.0 + bernoulli_bts_prob: 0.1 + bernoulli_eos_prob: 0.1 + # Buffer buffer: data_path: null train: null test: type: grid - n: 1000 - output_csv: ccube_test.csv - output_pkl: ccube_test.pkl + n: 900 + output_csv: clp_test.csv + output_pkl: clp_test.pkl diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index 9556d20cf..41f395539 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -52,8 +52,8 @@ def __init__( self, lattice_system: str, min_length: float = 1.0, - max_length: float = 5.0, - min_angle: float = 30.0, + max_length: float = 350.0, + min_angle: float = 50.0, max_angle: float = 150.0, **kwargs, ): From eefbb9912b575f27d53b26401bf66e875e3e7a60 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 00:37:32 -0400 Subject: [PATCH 030/205] statebatch2proxy --- gflownet/envs/crystals/clattice_parameters.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index 41f395539..da2eed32e 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -10,7 +10,7 @@ from gflownet.envs.crystals.lattice_parameters import LatticeParameters from gflownet.envs.cube import ContinuousCube -from gflownet.utils.common import copy +from gflownet.utils.common import copy, tfloat from gflownet.utils.crystals.constants import ( CUBIC, HEXAGONAL, @@ -268,3 +268,15 @@ def readable2state(self, readable: str) -> List[int]: for param, value in zip(PARAMETER_NAMES, values): state = self._set_param(state, param, value) return state + + def statebatch2proxy( + self, states: List[List] + ) -> TensorType["batch", "state_proxy_dim"]: + """ + Prepares a batch of states in "GFlowNet format" for the proxy: a tensor where + lengths and angles are converted into the target units (angstroms and degrees, + respectively). + """ + return self.statetorch2proxy( + tfloat(states, float_type=self.float, device=self.device) + ) From b7e65c6524227b8353a70772ceb3734a4547a403 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 18:58:14 -0400 Subject: [PATCH 031/205] Make attributes optional --- gflownet/envs/crystals/clattice_parameters.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index da2eed32e..a1882689a 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -51,17 +51,18 @@ class CLatticeParameters(ContinuousCube): def __init__( self, lattice_system: str, - min_length: float = 1.0, - max_length: float = 350.0, - min_angle: float = 50.0, - max_angle: float = 150.0, + min_length: Optional[float] = 1.0, + max_length: Optional[float] = 350.0, + min_angle: Optional[float] = 50.0, + max_angle: Optional[float] = 150.0, **kwargs, ): """ Args ---- lattice_system : str - One of the seven lattice systems. + One of the seven lattice systems. By default, the triclinic lattice system + is used, which has no constraints. min_length : float Minimum value of the lengths. From 8759c5fb669c4cdad53390bc27641191526617c1 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 18:58:43 -0400 Subject: [PATCH 032/205] First changes to adapt crystal env to continuous lattice parameters. --- gflownet/envs/crystals/ccrystal.py | 556 +++++++++++++++++++++++++++++ 1 file changed, 556 insertions(+) create mode 100644 gflownet/envs/crystals/ccrystal.py diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py new file mode 100644 index 000000000..814fe2cb5 --- /dev/null +++ b/gflownet/envs/crystals/ccrystal.py @@ -0,0 +1,556 @@ +import json +from enum import Enum +from typing import Dict, List, Optional, Tuple, Union + +import torch +from torch import Tensor +from torchtyping import TensorType + +from gflownet.envs.base import GFlowNetEnv +from gflownet.envs.crystals.composition import Composition +from gflownet.envs.crystals.clattice_parameters import CLatticeParameters +from gflownet.envs.crystals.spacegroup import SpaceGroup +from gflownet.utils.crystals.constants import TRICLINIC + + +class Stage(Enum): + """ + In addition to encoding current stage, contains methods used for padding individual + component environment's actions (to ensure they have the same length for + tensorization). + """ + + COMPOSITION = 0 + SPACE_GROUP = 1 + LATTICE_PARAMETERS = 2 + + def to_pad(self) -> int: + """ + Maps stage value to a padding. The following mapping is used: + + COMPOSITION = -2 + SPACE_GROUP = -3 + LATTICE_PARAMETERS = -4 + + We use negative numbers starting from -2 because they are not used by any of + the underlying environments, which should lead to every padded action being + unique. + """ + return -(self.value + 2) + + @classmethod + def from_pad(cls, pad_value: int) -> "Stage": + return Stage(-pad_value - 2) + + +class Crystal(GFlowNetEnv): + """ + A combination of Composition, SpaceGroup and CLatticeParameters into a single + environment. Works sequentially, by first filling in the Composition, then + SpaceGroup, and finally LatticeParameters. + """ + + def __init__( + self, + composition_kwargs: Optional[Dict] = None, + space_group_kwargs: Optional[Dict] = None, + lattice_parameters_kwargs: Optional[Dict] = None, + do_stoichiometry_sg_check: bool = False, + **kwargs, + ): + self.composition_kwargs = composition_kwargs or {} + self.space_group_kwargs = space_group_kwargs or {} + self.lattice_parameters_kwargs = lattice_parameters_kwargs or {} + self.do_stoichiometry_sg_check = do_stoichiometry_sg_check + + self.composition = Composition(**self.composition_kwargs) + self.space_group = SpaceGroup(**self.space_group_kwargs) + # We initialize lattice parameters with triclinic lattice system as it is the + # most general one (and the default), but it will have to be reinitialized using + # proper lattice system from space group once that is determined. + self.lattice_parameters = LatticeParameters( + lattice_system=TRICLINIC, **self.lattice_parameters_kwargs + ) + + # 0-th element of state encodes current stage: 0 for composition, + # 1 for space group, 2 for lattice parameters + self.source = ( + [Stage.COMPOSITION.value] + + self.composition.source + + self.space_group.source + + self.lattice_parameters.source + ) + + # start and end indices of individual substates + self.composition_state_start = 1 + self.composition_state_end = self.composition_state_start + len( + self.composition.source + ) + self.space_group_state_start = self.composition_state_end + self.space_group_state_end = self.space_group_state_start + len( + self.space_group.source + ) + self.lattice_parameters_state_start = self.space_group_state_end + self.lattice_parameters_state_end = self.lattice_parameters_state_start + len( + self.lattice_parameters.source + ) + + # start and end indices of individual submasks + self.composition_mask_start = 0 + self.composition_mask_end = self.composition_mask_start + len( + self.composition.action_space + ) + self.space_group_mask_start = self.composition_mask_end + self.space_group_mask_end = self.space_group_mask_start + len( + self.space_group.action_space + ) + self.lattice_parameters_mask_start = self.space_group_mask_end + self.lattice_parameters_mask_end = self.lattice_parameters_mask_start + len( + self.lattice_parameters.action_space + ) + + self.composition_action_length = max( + len(a) for a in self.composition.action_space + ) + self.space_group_action_length = max( + len(a) for a in self.space_group.action_space + ) + self.lattice_parameters_action_length = max( + len(a) for a in self.lattice_parameters.action_space + ) + self.max_action_length = max( + self.composition_action_length, + self.space_group_action_length, + self.lattice_parameters_action_length, + ) + + # EOS is EOS of LatticeParameters because it is the last stage + self.eos = self._pad_action( + self.lattice_parameters.eos, Stage.LATTICE_PARAMETERS + ) + + # Conversions + self.state2proxy = self.state2oracle + self.statebatch2proxy = self.statebatch2oracle + self.statetorch2proxy = self.statetorch2oracle + + super().__init__(**kwargs) + + def _set_lattice_parameters(self): + """ + Sets CLatticeParameters conditioned on the lattice system derived from the + SpaceGroup. + """ + if self.space_group.lattice_system == "None": + raise ValueError( + "Cannot set lattice parameters without lattice system determined in " + "the space group." + ) + + self.lattice_parameters = CLatticeParameters( + lattice_system=self.space_group.lattice_system, + **self.lattice_parameters_kwargs, + ) + + def _pad_action(self, action: Tuple[int], stage: Stage) -> Tuple[int]: + """ + Pads action such that all actions, regardless of the underlying environment, + have the same length. Required due to the fact that action space has to be + convertable to a tensor. + """ + return action + (Stage.to_pad(stage),) * (self.max_action_length - len(action)) + + def _pad_action_space( + self, action_space: List[Tuple[int]], stage: Stage + ) -> List[Tuple[int]]: + return [self._pad_action(a, stage) for a in action_space] + + def _depad_action(self, action: Tuple[int], stage: Stage) -> Tuple[int]: + """ + Reverses padding operation, such that the resulting action can be passed to the + underlying environment. + """ + if stage == Stage.COMPOSITION: + dim = self.composition_action_length + elif stage == Stage.SPACE_GROUP: + dim = self.space_group_action_length + elif stage == Stage.LATTICE_PARAMETERS: + dim = self.lattice_parameters_action_length + else: + raise ValueError(f"Unrecognized stage {stage}.") + + return action[:dim] + + def get_action_space(self) -> List[Tuple[int]]: + composition_action_space = self._pad_action_space( + self.composition.action_space, Stage.COMPOSITION + ) + space_group_action_space = self._pad_action_space( + self.space_group.action_space, Stage.SPACE_GROUP + ) + lattice_parameters_action_space = self._pad_action_space( + self.lattice_parameters.action_space, Stage.LATTICE_PARAMETERS + ) + + action_space = ( + composition_action_space + + space_group_action_space + + lattice_parameters_action_space + ) + + if len(action_space) != len(set(action_space)): + raise ValueError( + "Detected duplicate actions between different components of Crystal " + "environment." + ) + + return action_space + + def get_max_traj_length(self) -> int: + return ( + self.composition.get_max_traj_length() + + self.space_group.get_max_traj_length() + + self.lattice_parameters.get_max_traj_length() + ) + + def reset(self, env_id: Union[int, str] = None): + self.composition.reset() + self.space_group.reset() + self.lattice_parameters = LatticeParameters( + lattice_system=TRICLINIC, **self.lattice_parameters_kwargs + ) + + super().reset(env_id=env_id) + self._set_stage(Stage.COMPOSITION) + + return self + + def _get_stage(self, state: Optional[List] = None) -> Stage: + """ + Returns the stage of the current environment from self.state[0] or from the + state passed as an argument. + """ + state = self._get_state(state) + return Stage(state[0]) + + def _set_stage(self, stage: Stage, state: Optional[List] = None): + """ + Sets the stage of the current environment (self.state) or of the state passed + as an argument by updating state[0]. + """ + state = self._get_state(state) + state[0] = stage.value + + def _get_composition_state(self, state: Optional[List[int]] = None) -> List[int]: + state = self._get_state(state) + + return state[self.composition_state_start : self.composition_state_end] + + def _get_composition_tensor_states( + self, states: TensorType["batch", "state_dim"] + ) -> TensorType["batch", "state_oracle_dim"]: + return states[:, self.composition_state_start : self.composition_state_end] + + def _get_space_group_state(self, state: Optional[List[int]] = None) -> List[int]: + state = self._get_state(state) + + return state[self.space_group_state_start : self.space_group_state_end] + + def _get_space_group_tensor_states( + self, states: TensorType["batch", "state_dim"] + ) -> TensorType["batch", "state_oracle_dim"]: + return states[:, self.space_group_state_start : self.space_group_state_end] + + def _get_lattice_parameters_state( + self, state: Optional[List[int]] = None + ) -> List[int]: + state = self._get_state(state) + + return state[ + self.lattice_parameters_state_start : self.lattice_parameters_state_end + ] + + def _get_lattice_parameters_tensor_states( + self, states: TensorType["batch", "state_dim"] + ) -> TensorType["batch", "state_oracle_dim"]: + return states[ + :, self.lattice_parameters_state_start : self.lattice_parameters_state_end + ] + + def get_mask_invalid_actions_forward( + self, state: Optional[List[int]] = None, done: Optional[bool] = None + ) -> List[bool]: + state = self._get_state(state) + done = self._get_done(done) + stage = self._get_stage(state) + + if done: + return [True] * self.action_space_dim + + mask = [True] * self.action_space_dim + + if stage == Stage.COMPOSITION: + composition_mask = self.composition.get_mask_invalid_actions_forward( + state=self._get_composition_state(state), done=False + ) + mask[ + self.composition_mask_start : self.composition_mask_end + ] = composition_mask + elif stage == Stage.SPACE_GROUP: + space_group_state = self._get_space_group_state(state) + space_group_mask = self.space_group.get_mask_invalid_actions_forward( + state=space_group_state, done=False + ) + mask[ + self.space_group_mask_start : self.space_group_mask_end + ] = space_group_mask + elif stage == Stage.LATTICE_PARAMETERS: + """ + TODO: to be stateless (meaning, operating as a function, not a method with + current object context) this needs to set lattice system based on the passed + state only. Right now it uses the current LatticeParameter environment, in + particular the lattice system that it was set to, and that changes the invalid + actions mask. + + If for some reason a state will be passed to this method that describes an + object with different lattice system than what self.lattice_system contains, + the result will be invalid. + """ + lattice_parameters_state = self._get_lattice_parameters_state(state) + lattice_parameters_mask = ( + self.lattice_parameters.get_mask_invalid_actions_forward( + state=lattice_parameters_state, done=False + ) + ) + mask[ + self.lattice_parameters_mask_start : self.lattice_parameters_mask_end + ] = lattice_parameters_mask + else: + raise ValueError(f"Unrecognized stage {stage}.") + + return mask + + def _update_state(self): + """ + Updates current state based on the states of underlying environments. + """ + self.state = ( + [self._get_stage(self.state).value] + + self.composition.state + + self.space_group.state + + self.lattice_parameters.state + ) + + def step(self, action: Tuple[int]) -> Tuple[List[int], Tuple[int], bool]: + # If action not found in action space raise an error + if action not in self.action_space: + raise ValueError( + f"Tried to execute action {action} not present in action space." + ) + else: + action_idx = self.action_space.index(action) + # If action is in invalid mask, exit immediately + if self.get_mask_invalid_actions_forward()[action_idx]: + return self.state, action, False + self.n_actions += 1 + + stage = self._get_stage(self.state) + if stage == Stage.COMPOSITION: + composition_action = self._depad_action(action, Stage.COMPOSITION) + _, executed_action, valid = self.composition.step(composition_action) + if valid and executed_action == self.composition.eos: + self._set_stage(Stage.SPACE_GROUP) + if self.do_stoichiometry_sg_check: + self.space_group.set_n_atoms_compatibility_dict( + self.composition.state + ) + elif stage == Stage.SPACE_GROUP: + stage_group_action = self._depad_action(action, Stage.SPACE_GROUP) + _, executed_action, valid = self.space_group.step(stage_group_action) + if valid and executed_action == self.space_group.eos: + self._set_stage(Stage.LATTICE_PARAMETERS) + self._set_lattice_parameters() + elif stage == Stage.LATTICE_PARAMETERS: + lattice_parameters_action = self._depad_action( + action, Stage.LATTICE_PARAMETERS + ) + _, executed_action, valid = self.lattice_parameters.step( + lattice_parameters_action + ) + if valid and executed_action == self.lattice_parameters.eos: + self.done = True + else: + raise ValueError(f"Unrecognized stage {stage}.") + + self._update_state() + + return self.state, action, valid + + def _build_state(self, substate: List, stage: Stage) -> List: + """ + Converts the state coming from one of the subenvironments into a combined state + format used by the Crystal environment. + """ + if stage == Stage.COMPOSITION: + output = ( + [0] + + substate + + self.space_group.source + + self.lattice_parameters.source + ) + elif stage == Stage.SPACE_GROUP: + output = ( + [1] + self.composition.state + substate + [0] * 6 + ) # hard-code LatticeParameters` source, since it can change with other lattice system + elif stage == Stage.LATTICE_PARAMETERS: + output = [2] + self.composition.state + self.space_group.state + substate + else: + raise ValueError(f"Unrecognized stage {stage}.") + + return output + + def get_parents( + self, + state: Optional[List] = None, + done: Optional[bool] = None, + action: Optional[Tuple] = None, + ) -> Tuple[List, List]: + state = self._get_state(state) + done = self._get_done(done) + stage = self._get_stage(state) + + if done: + return [state], [self.eos] + + if stage == Stage.COMPOSITION or ( + stage == Stage.SPACE_GROUP + and self._get_space_group_state(state) == self.space_group.source + ): + composition_done = stage == Stage.SPACE_GROUP + parents, actions = self.composition.get_parents( + state=self._get_composition_state(state), done=composition_done + ) + parents = [self._build_state(p, Stage.COMPOSITION) for p in parents] + actions = [self._pad_action(a, Stage.COMPOSITION) for a in actions] + elif stage == Stage.SPACE_GROUP or ( + stage == Stage.LATTICE_PARAMETERS + and self._get_lattice_parameters_state(state) + == self.lattice_parameters.source + ): + space_group_done = stage == Stage.LATTICE_PARAMETERS + parents, actions = self.space_group.get_parents( + state=self._get_space_group_state(state), done=space_group_done + ) + parents = [self._build_state(p, Stage.SPACE_GROUP) for p in parents] + actions = [self._pad_action(a, Stage.SPACE_GROUP) for a in actions] + elif stage == Stage.LATTICE_PARAMETERS: + """ + TODO: to be stateless (meaning, operating as a function, not a method with + current object context) this needs to set lattice system based on the passed + state only. Right now it uses the current LatticeParameter environment, in + particular the lattice system that it was set to, and that changes the invalid + actions mask. + + If for some reason a state will be passed to this method that describes an + object with different lattice system than what self.lattice_system contains, + the result will be invalid. + """ + parents, actions = self.lattice_parameters.get_parents( + state=self._get_lattice_parameters_state(state), done=done + ) + parents = [self._build_state(p, Stage.LATTICE_PARAMETERS) for p in parents] + actions = [self._pad_action(a, Stage.LATTICE_PARAMETERS) for a in actions] + else: + raise ValueError(f"Unrecognized stage {stage}.") + + return parents, actions + + def state2oracle(self, state: Optional[List[int]] = None) -> Tensor: + """ + Prepares a list of states in "GFlowNet format" for the oracle. Simply + a concatenation of all crystal components. + """ + if state is None: + state = self.state.copy() + + composition_oracle_state = self.composition.state2oracle( + state=self._get_composition_state(state) + ).to(self.device) + space_group_oracle_state = ( + self.space_group.state2oracle(state=self._get_space_group_state(state)) + .unsqueeze(-1) # StateGroup oracle state is a single number + .to(self.device) + ) + lattice_parameters_oracle_state = self.lattice_parameters.state2oracle( + state=self._get_lattice_parameters_state(state) + ).to(self.device) + + return torch.cat( + [ + composition_oracle_state, + space_group_oracle_state, + lattice_parameters_oracle_state, + ] + ) + + def statebatch2oracle( + self, states: List[List] + ) -> TensorType["batch", "state_oracle_dim"]: + return self.statetorch2oracle( + torch.tensor(states, device=self.device, dtype=torch.long) + ) + + def statetorch2oracle( + self, states: TensorType["batch", "state_dim"] + ) -> TensorType["batch", "state_oracle_dim"]: + composition_oracle_states = self.composition.statetorch2oracle( + self._get_composition_tensor_states(states) + ).to(self.device) + space_group_oracle_states = self.space_group.statetorch2oracle( + self._get_space_group_tensor_states(states) + ).to(self.device) + lattice_parameters_oracle_states = self.lattice_parameters.statetorch2oracle( + self._get_lattice_parameters_tensor_states(states) + ).to(self.device) + return torch.cat( + [ + composition_oracle_states, + space_group_oracle_states, + lattice_parameters_oracle_states, + ], + dim=1, + ) + + def state2readable(self, state: Optional[List[int]] = None) -> str: + if state is None: + state = self.state + + composition_readable = self.composition.state2readable( + state=self._get_composition_state(state) + ) + space_group_readable = self.space_group.state2readable( + state=self._get_space_group_state(state) + ) + lattice_parameters_readable = self.lattice_parameters.state2readable( + state=self._get_lattice_parameters_state(state) + ) + + return ( + f"Stage = {state[0]}; " + f"Composition = {composition_readable}; " + f"SpaceGroup = {space_group_readable}; " + f"LatticeParameters = {lattice_parameters_readable}" + ) + + def readable2state(self, readable: str) -> List[int]: + splits = readable.split("; ") + readables = [x.split(" = ")[1] for x in splits] + + return ( + [int(readables[0])] + + self.composition.readable2state( + json.loads(readables[1].replace("'", '"')) + ) + + self.space_group.readable2state(readables[2]) + + self.lattice_parameters.readable2state(readables[3]) + ) From a310460ef56acff8fa1cde1c3ecacb6a8afed011 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 19:01:23 -0400 Subject: [PATCH 033/205] Merge changes from main --- gflownet/envs/crystals/ccrystal.py | 39 ++++++++++++++++++++++++++++-- 1 file changed, 37 insertions(+), 2 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 814fe2cb5..bb06efeea 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -66,8 +66,8 @@ def __init__( self.composition = Composition(**self.composition_kwargs) self.space_group = SpaceGroup(**self.space_group_kwargs) # We initialize lattice parameters with triclinic lattice system as it is the - # most general one (and the default), but it will have to be reinitialized using - # proper lattice system from space group once that is determined. + # most general one, but it will have to be reinitialized using proper lattice + # system from space group once that is determined. self.lattice_parameters = LatticeParameters( lattice_system=TRICLINIC, **self.lattice_parameters_kwargs ) @@ -521,6 +521,41 @@ def statetorch2oracle( dim=1, ) + def set_state(self, state: List, done: Optional[bool] = False): + super().set_state(state, done) + + stage = self._get_stage(state) + + composition_done = stage in [Stage.SPACE_GROUP, Stage.LATTICE_PARAMETERS] + space_group_done = stage == Stage.LATTICE_PARAMETERS + lattice_parameters_done = done + + self.composition.set_state(self._get_composition_state(state), composition_done) + self.space_group.set_state(self._get_space_group_state(state), space_group_done) + self.lattice_parameters.set_state( + self._get_lattice_parameters_state(state), lattice_parameters_done + ) + + """ + We synchronize LatticeParameter's lattice system with the one of SpaceGroup + (if it was set) or reset it to the default triclinic otherwise. Why this is + needed: + 1) the first case is necessary for backward sampling, where we start from + an arbitrary terminal state, and need to synchronize the LatticeParameter's + lattice system to what that state indicates, + 2) the second case is also necessary in backward sampling, but when we + transition from Stage.LATTICE_PARAMETERS to Stage.SPACE_GROUP. We then need + to reset the lattice system to the default triclinic, such that its + source is back to the original one, and corresponds to the source of the + general Crystal environment. + """ + lattice_system = self.space_group.lattice_system + if lattice_system != "None": + self.lattice_parameters.lattice_system = lattice_system + else: + self.lattice_parameters.lattice_system = TRICLINIC + self.lattice_parameters._set_source() + def state2readable(self, state: Optional[List[int]] = None) -> str: if state is None: state = self.state From 3740aaff73e6da6c61a817b62a2cf884f5c9b464 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 19:07:26 -0400 Subject: [PATCH 034/205] Further changes to adapt the crystal env to continuous parameters. --- gflownet/envs/crystals/ccrystal.py | 39 ++++++++++++------------------ 1 file changed, 16 insertions(+), 23 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index bb06efeea..a4a00b758 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -47,7 +47,7 @@ class Crystal(GFlowNetEnv): """ A combination of Composition, SpaceGroup and CLatticeParameters into a single environment. Works sequentially, by first filling in the Composition, then - SpaceGroup, and finally LatticeParameters. + SpaceGroup, and finally CLatticeParameters. """ def __init__( @@ -307,14 +307,14 @@ def get_mask_invalid_actions_forward( elif stage == Stage.LATTICE_PARAMETERS: """ TODO: to be stateless (meaning, operating as a function, not a method with - current object context) this needs to set lattice system based on the passed - state only. Right now it uses the current LatticeParameter environment, in - particular the lattice system that it was set to, and that changes the invalid - actions mask. + current object context) this needs to set lattice system based on the + passed state only. Right now it uses the current LatticeParameter + environment, in particular the lattice system that it was set to, and that + changes the invalid actions mask. If for some reason a state will be passed to this method that describes an - object with different lattice system than what self.lattice_system contains, - the result will be invalid. + object with different lattice system than what self.lattice_system + contains, the result will be invalid. """ lattice_parameters_state = self._get_lattice_parameters_state(state) lattice_parameters_mask = ( @@ -400,8 +400,8 @@ def _build_state(self, substate: List, stage: Stage) -> List: ) elif stage == Stage.SPACE_GROUP: output = ( - [1] + self.composition.state + substate + [0] * 6 - ) # hard-code LatticeParameters` source, since it can change with other lattice system + [1] + self.composition.state + substate + self.lattice_parameters.source + ) elif stage == Stage.LATTICE_PARAMETERS: output = [2] + self.composition.state + self.space_group.state + substate else: @@ -446,10 +446,10 @@ def get_parents( elif stage == Stage.LATTICE_PARAMETERS: """ TODO: to be stateless (meaning, operating as a function, not a method with - current object context) this needs to set lattice system based on the passed - state only. Right now it uses the current LatticeParameter environment, in - particular the lattice system that it was set to, and that changes the invalid - actions mask. + current object context) this needs to set lattice system based on the + passed state only. Right now it uses the current LatticeParameter + environment, in particular the lattice system that it was set to, and that + changes the invalid actions mask. If for some reason a state will be passed to this method that describes an object with different lattice system than what self.lattice_system contains, @@ -539,22 +539,15 @@ def set_state(self, state: List, done: Optional[bool] = False): """ We synchronize LatticeParameter's lattice system with the one of SpaceGroup (if it was set) or reset it to the default triclinic otherwise. Why this is - needed: - 1) the first case is necessary for backward sampling, where we start from - an arbitrary terminal state, and need to synchronize the LatticeParameter's - lattice system to what that state indicates, - 2) the second case is also necessary in backward sampling, but when we - transition from Stage.LATTICE_PARAMETERS to Stage.SPACE_GROUP. We then need - to reset the lattice system to the default triclinic, such that its - source is back to the original one, and corresponds to the source of the - general Crystal environment. + needed: for backward sampling, where we start from an arbitrary terminal state, + and need to synchronize the LatticeParameter's lattice system to what that + state indicates, """ lattice_system = self.space_group.lattice_system if lattice_system != "None": self.lattice_parameters.lattice_system = lattice_system else: self.lattice_parameters.lattice_system = TRICLINIC - self.lattice_parameters._set_source() def state2readable(self, state: Optional[List[int]] = None) -> str: if state is None: From 5844f15b540fa6f935bb55f147ed9e8ed5ef70fd Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 20:08:30 -0400 Subject: [PATCH 035/205] Fixes --- gflownet/envs/crystals/ccrystal.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index a4a00b758..60c722f63 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -43,7 +43,7 @@ def from_pad(cls, pad_value: int) -> "Stage": return Stage(-pad_value - 2) -class Crystal(GFlowNetEnv): +class CCrystal(GFlowNetEnv): """ A combination of Composition, SpaceGroup and CLatticeParameters into a single environment. Works sequentially, by first filling in the Composition, then @@ -68,7 +68,7 @@ def __init__( # We initialize lattice parameters with triclinic lattice system as it is the # most general one, but it will have to be reinitialized using proper lattice # system from space group once that is determined. - self.lattice_parameters = LatticeParameters( + self.lattice_parameters = CLatticeParameters( lattice_system=TRICLINIC, **self.lattice_parameters_kwargs ) @@ -216,7 +216,7 @@ def get_max_traj_length(self) -> int: def reset(self, env_id: Union[int, str] = None): self.composition.reset() self.space_group.reset() - self.lattice_parameters = LatticeParameters( + self.lattice_parameters = CLatticeParameters( lattice_system=TRICLINIC, **self.lattice_parameters_kwargs ) @@ -230,7 +230,8 @@ def _get_stage(self, state: Optional[List] = None) -> Stage: Returns the stage of the current environment from self.state[0] or from the state passed as an argument. """ - state = self._get_state(state) + if state is None: + state = self.state return Stage(state[0]) def _set_stage(self, stage: Stage, state: Optional[List] = None): @@ -238,7 +239,8 @@ def _set_stage(self, stage: Stage, state: Optional[List] = None): Sets the stage of the current environment (self.state) or of the state passed as an argument by updating state[0]. """ - state = self._get_state(state) + if state is None: + state = self.state state[0] = stage.value def _get_composition_state(self, state: Optional[List[int]] = None) -> List[int]: From d2a0c80870d3b77e887b229f9e906dc1af8048e6 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 20:36:45 -0400 Subject: [PATCH 036/205] Implement action2representative in cube and crystal --- gflownet/envs/crystals/ccrystal.py | 36 ++++++++++++++++++++---------- gflownet/envs/cube.py | 10 +++++++++ 2 files changed, 34 insertions(+), 12 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 60c722f63..36bf65652 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -7,8 +7,8 @@ from torchtyping import TensorType from gflownet.envs.base import GFlowNetEnv -from gflownet.envs.crystals.composition import Composition from gflownet.envs.crystals.clattice_parameters import CLatticeParameters +from gflownet.envs.crystals.composition import Composition from gflownet.envs.crystals.spacegroup import SpaceGroup from gflownet.utils.crystals.constants import TRICLINIC @@ -206,6 +206,18 @@ def get_action_space(self) -> List[Tuple[int]]: return action_space + def action2representative(self, action: Tuple) -> Tuple: + """ + Replaces the continuous values of lattice parameters actions by the + representative action of the environment so that it can be compared against the + action space. + """ + if self._get_stage() == Stage.LATTICE_PARAMETERS: + return self.lattice_parameters.action2representative( + self._depad_action(action, Stage.LATTICE_PARAMETERS) + ) + return action + def get_max_traj_length(self) -> int: return ( self.composition.get_max_traj_length() @@ -343,19 +355,19 @@ def _update_state(self): + self.lattice_parameters.state ) - def step(self, action: Tuple[int]) -> Tuple[List[int], Tuple[int], bool]: - # If action not found in action space raise an error - if action not in self.action_space: - raise ValueError( - f"Tried to execute action {action} not present in action space." - ) - else: - action_idx = self.action_space.index(action) - # If action is in invalid mask, exit immediately - if self.get_mask_invalid_actions_forward()[action_idx]: + def step( + self, action: Tuple[int], skip_mask_check: bool = False + ) -> Tuple[List[int], Tuple[int], bool]: + # Replace action by its representative to check against the mask. + action_to_check = self.action2representative(action) + do_step, self.state, action_to_check = self._pre_step( + action_to_check, + skip_mask_check=(skip_mask_check or self.skip_mask_check), + ) + if not do_step: return self.state, action, False - self.n_actions += 1 + self.n_actions += 1 stage = self._get_stage(self.state) if stage == Stage.COMPOSITION: composition_action = self._depad_action(action, Stage.COMPOSITION) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 1225d4db7..7e64b906b 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -1350,6 +1350,16 @@ def step_backwards( self._step(action, backward=True) return self.state, action, True + def action2representative(self, action: Tuple) -> Tuple: + """ + Replaces the continuous values of an action by 0s (the "generic" or + "representative" action in the first position of the action space), so that + they can be compared against the action space or a mask. + """ + if action != self.eos: + return self.action_space[0] + return action + def get_grid_terminating_states( self, n_states: int, kappa: Optional[float] = None ) -> List[List]: From 4ae544b9fadfe1c0df29b2df7d33073e14e001d6 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 21:15:10 -0400 Subject: [PATCH 037/205] Changes in step methods: return invalid if out of bounds. --- gflownet/envs/cube.py | 60 ++++++++++++++++++++++--------------------- 1 file changed, 31 insertions(+), 29 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 7e64b906b..3909d9dc7 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -2,6 +2,7 @@ Classes to represent hyper-cube environments """ import itertools +import warnings from abc import ABC, abstractmethod from typing import List, Optional, Tuple @@ -351,6 +352,9 @@ def get_action_space(self): self.representative_action = tuple([0.0] * actions_dim) return [self.representative_action, self.eos] + def get_max_traj_length(self): + return np.ceil(1.0 / self.min_incr) + 2 + def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: """ Defines the structure of the output of the policy model, from which an @@ -1245,32 +1249,30 @@ def _step( root state """ # If forward action is from source, initialize state to all zeros. - if not backward and action[-1] == 1: - self.state = [0.0 for _ in range(self.n_dim)] + if not backward and action[-1] == 1 and self.state == self.source: + state = [0.0 for _ in range(self.n_dim)] + else: + state = copy(self.state) # Increment dimensions for dim, incr in enumerate(action[:-1]): if backward: - self.state[dim] -= incr + state[dim] -= incr else: - self.state[dim] += incr - # If backward action is to source, set state to source - if backward and action[-1] == 1: - self.state = self.source + state[dim] += incr + + # If state is out of bounds, return invalid + if any([s > 1.0 for s in state]) or any([s < 0.0 for s in state]): + warnings.warn( + f""" + State is out of cube bounds. + \nCurrent state:\n{self.state}\nAction:\n{action}\nNext state: {state} + """ + ) + return self.state, action, False - # Check that state is within bounds - if self.state != self.source: - assert all( - [s <= 1.0 for s in self.state] - ), f""" - State is out of cube bounds. - \nState:\n{self.state}\nAction:\n{action}\nIncrement: {incr} - """ - assert all( - [s >= 0.0 for s in self.state] - ), f""" - State is out of cube bounds. - \nState:\n{self.state}\nAction:\n{action}\nIncrement: {incr} - """ + # Otherwise, set self.state as the udpated state and return valid. + self.n_actions += 1 + self.state = state return self.state, action, True # TODO: make generic for continuous environments? @@ -1308,11 +1310,8 @@ def step(self, action: Tuple[float]) -> Tuple[List[float], Tuple[int, float], bo return self.state, self.eos, True # Otherwise perform action else: - self.n_actions += 1 - self._step(action, backward=False) - return self.state, action, True + return self._step(action, backward=False) - # TODO: make generic for continuous environments? def step_backwards( self, action: Tuple[int, float] ) -> Tuple[List[float], Tuple[int, float], bool]: @@ -1344,11 +1343,14 @@ def step_backwards( self.done = False self.n_actions += 1 return self.state, action, True - # Otherwise perform action assert action != self.eos - self.n_actions += 1 - self._step(action, backward=True) - return self.state, action, True + # If action is BTS, set source state + if action[-1] == 1 and self.state != self.source: + self.n_actions += 1 + self.state = self.source + return self.state, action, True + # Otherwise perform action + return self._step(action, backward=True) def action2representative(self, action: Tuple) -> Tuple: """ From e8b20aa7090c3fec95262bf08d9a18befdd56f1f Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 21:59:12 -0400 Subject: [PATCH 038/205] Small changes in cube --- gflownet/envs/cube.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 3909d9dc7..84a5f2888 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -1252,6 +1252,7 @@ def _step( if not backward and action[-1] == 1 and self.state == self.source: state = [0.0 for _ in range(self.n_dim)] else: + assert action[-1] == 0 state = copy(self.state) # Increment dimensions for dim, incr in enumerate(action[:-1]): @@ -1358,9 +1359,7 @@ def action2representative(self, action: Tuple) -> Tuple: "representative" action in the first position of the action space), so that they can be compared against the action space or a mask. """ - if action != self.eos: - return self.action_space[0] - return action + return self.action_space[0] def get_grid_terminating_states( self, n_states: int, kappa: Optional[float] = None From 4dba034da92928973085e03f49f23520f9391b57 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 22 Sep 2023 22:00:16 -0400 Subject: [PATCH 039/205] WIP: tests for continuous crystals. --- tests/gflownet/envs/test_ccrystal.py | 405 +++++++++++++++++++++++++++ 1 file changed, 405 insertions(+) create mode 100644 tests/gflownet/envs/test_ccrystal.py diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py new file mode 100644 index 000000000..b3a0d29bd --- /dev/null +++ b/tests/gflownet/envs/test_ccrystal.py @@ -0,0 +1,405 @@ +import common +import pytest +import torch +import warnings +import numpy as np +from torch import Tensor + +from gflownet.envs.crystals.ccrystal import CCrystal, Stage +from gflownet.envs.crystals.clattice_parameters import TRICLINIC + + +@pytest.fixture +def env(): + return CCrystal( + composition_kwargs={"elements": 4} + ) + + +@pytest.fixture +def env_with_stoichiometry_sg_check(): + return CCrystal( + composition_kwargs={"elements": 4}, + do_stoichiometry_sg_check=True, + ) + + +def test__environment__initializes_properly(env): + pass + + +def test__environment__has_expected_initial_state(env): + """ + The source of the composition and space group environments is all 0s. The source of + the continuous lattice parameters environment is all -1s. + """ + assert ( + env.state == env.source == [0] * (1 + 4 + 3) + [-1] * 6 + ) # stage + n elements + space groups + lattice parameters + + +def test__environment__has_expected_action_space(env): + assert len(env.action_space) == len(env.composition.action_space) + len( + env.space_group.action_space + ) + len(env.lattice_parameters.action_space) + + underlying_action_space = ( + env.composition.action_space + + env.space_group.action_space + + env.lattice_parameters.action_space + ) + + for action, underlying_action in zip(env.action_space, underlying_action_space): + assert action[: len(underlying_action)] == underlying_action + + +def test__pad_depad_action(env): + for subenv, stage in [ + (env.composition, Stage.COMPOSITION), + (env.space_group, Stage.SPACE_GROUP), + (env.lattice_parameters, Stage.LATTICE_PARAMETERS), + ]: + for action in subenv.action_space: + padded = env._pad_action(action, stage) + assert len(padded) == env.max_action_length + depadded = env._depad_action(padded, stage) + assert depadded == action + + +@pytest.mark.skip(reason="skip until updated") +@pytest.mark.parametrize( + "state, expected", + [ + [ + (2, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 4, 5, 6), + Tensor([1.0, 1.0, 1.0, 1.0, 3.0, 1.4, 1.8, 2.2, 78.0, 90.0, 102.0]), + ], + [ + (2, 4, 9, 0, 3, 0, 0, 105, 5, 3, 1, 0, 0, 9), + Tensor([4.0, 9.0, 0.0, 3.0, 105.0, 3.0, 2.2, 1.4, 30.0, 30.0, 138.0]), + ], + ], +) +def test__state2oracle__returns_expected_value(env, state, expected): + assert torch.allclose(env.state2oracle(state), expected, atol=1e-4) + + +@pytest.mark.skip(reason="skip until updated") +@pytest.mark.parametrize( + "state, expected", + [ + [ + (2, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 4, 5, 6), + Tensor([1.0, 1.0, 1.0, 1.0, 3.0, 1.4, 1.8, 2.2, 78.0, 90.0, 102.0]), + ], + [ + (2, 4, 9, 0, 3, 0, 0, 105, 5, 3, 1, 0, 0, 9), + Tensor([4.0, 9.0, 0.0, 3.0, 105.0, 3.0, 2.2, 1.4, 30.0, 30.0, 138.0]), + ], + ], +) +def test__state2proxy__returns_expected_value(env, state, expected): + assert torch.allclose(env.state2proxy(state), expected, atol=1e-4) + + +@pytest.mark.skip(reason="skip until updated") +@pytest.mark.parametrize( + "batch, expected", + [ + [ + [ + (2, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 4, 5, 6), + (2, 4, 9, 0, 3, 0, 0, 105, 5, 3, 1, 0, 0, 9), + ], + Tensor( + [ + [1.0, 1.0, 1.0, 1.0, 3.0, 1.4, 1.8, 2.2, 78.0, 90.0, 102.0], + [4.0, 9.0, 0.0, 3.0, 105.0, 3.0, 2.2, 1.4, 30.0, 30.0, 138.0], + ] + ), + ], + ], +) +def test__statebatch2proxy__returns_expected_value(env, batch, expected): + assert torch.allclose(env.statebatch2proxy(batch), expected, atol=1e-4) + + +@pytest.mark.parametrize("action", [(1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2)]) +def test__step__single_action_works(env, action): + env.step(action) + + assert env.state != env.source + + +@pytest.mark.parametrize( + "actions, exp_result, exp_stage, last_action_valid", + [ + [ + [(1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2)], + [0, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.COMPOSITION, + True, + ], + [ + [(2, 225, 3, -3, -3, -3, -3)], + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.COMPOSITION, + False, + ], + [ + [(1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), (-1, -1, -2, -2, -2, -2, -2)], + [1, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.SPACE_GROUP, + True, + ], + [ + [ + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + ], + [1, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.SPACE_GROUP, + True, + ], + [ + [ + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + (2, 105, 0, -3, -3, -3, -3), + ], + [1, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.SPACE_GROUP, + False, + ], + [ + [ + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + ], + [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.LATTICE_PARAMETERS, + True, + ], + [ + [ + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (1.5, 0, 0, 0, 0, 0, 0), + ], + [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.LATTICE_PARAMETERS, + False, + ], + [ + [ + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + ], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + Stage.LATTICE_PARAMETERS, + True, + ], + [ + [ + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (0.6, 0.5, 0.4, 0.3, 0.2, 0.6, 0), + ], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + Stage.LATTICE_PARAMETERS, + False, + ], + [ + [ + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (0.66, 0.55, 0.44, 0.33, 0.22, 0.11, 0), + ], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + Stage.LATTICE_PARAMETERS, + True, + ], + [ + [ + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (0.66, 0.55, 0.44, 0.33, 0.22, 0.11, 0), + (np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf), + ], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + Stage.LATTICE_PARAMETERS, + True, + ], + ], +) +def test__step__action_sequence_has_expected_result( + env, actions, exp_result, exp_stage, last_action_valid +): + for action in actions: + warnings.filterwarnings("ignore") + _, _, valid = env.step(action) + + assert env.state == exp_result + assert env._get_stage() == exp_stage + assert valid == last_action_valid + + +# TODO: continue from here +def test__get_parents__returns_no_parents_in_initial_state(env): + return common.test__get_parents__returns_no_parents_in_initial_state(env) + + +@pytest.mark.skip(reason="skip until updated") +@pytest.mark.parametrize( + "actions", + [ + [(1, 1, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2)], + [ + (1, 1, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3), + (-1, -1, -1, -3, -3, -3), + (1, 1, 1, 0, 0, 0), + (1, 1, 0, 0, 0, 0), + (0, 0, 0, 0, 0, 0), + ], + ], +) +def test__get_parents__contains_previous_action_after_a_step(env, actions): + for action in actions: + env.step(action) + parents, parent_actions = env.get_parents() + assert action in parent_actions + + +@pytest.mark.skip(reason="skip until updated") +@pytest.mark.parametrize( + "actions", + [ + [ + (1, 1, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3), + (-1, -1, -1, -3, -3, -3), + (1, 1, 1, 0, 0, 0), + (1, 1, 0, 0, 0, 0), + (0, 0, 0, 0, 0, 0), + ] + ], +) +def test__reset(env, actions): + for action in actions: + env.step(action) + + assert env.state != env.source + for subenv in [env.composition, env.space_group, env.lattice_parameters]: + assert subenv.state != subenv.source + assert env.lattice_parameters.lattice_system != TRICLINIC + + env.reset() + + assert env.state == env.source + for subenv in [env.composition, env.space_group, env.lattice_parameters]: + assert subenv.state == subenv.source + assert env.lattice_parameters.lattice_system == TRICLINIC + + +@pytest.mark.skip(reason="skip until updated") +@pytest.mark.parametrize( + "actions, exp_stage", + [ + [ + [], + Stage.COMPOSITION, + ], + [ + [(1, 1, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2), (-1, -1, -2, -2, -2, -2)], + Stage.SPACE_GROUP, + ], + [ + [ + (1, 1, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3), + (-1, -1, -1, -3, -3, -3), + ], + Stage.LATTICE_PARAMETERS, + ], + ], +) +def test__get_mask_invalid_actions_forward__masks_all_actions_from_different_stages( + env, actions, exp_stage +): + for action in actions: + env.step(action) + + assert env._get_stage() == exp_stage + + mask = env.get_mask_invalid_actions_forward() + + if env._get_stage() == Stage.COMPOSITION: + assert not all(mask[: len(env.composition.action_space)]) + assert all(mask[len(env.composition.action_space) :]) + if env._get_stage() == Stage.SPACE_GROUP: + assert not all( + mask[ + len(env.composition.action_space) : len(env.composition.action_space) + + len(env.space_group.action_space) + ] + ) + assert all(mask[: len(env.composition.action_space)]) + assert all( + mask[ + len(env.composition.action_space) + len(env.space_group.action_space) : + ] + ) + if env._get_stage() == Stage.LATTICE_PARAMETERS: + assert not all( + mask[ + len(env.composition.action_space) + len(env.space_group.action_space) : + ] + ) + assert all( + mask[ + : len(env.composition.action_space) + len(env.space_group.action_space) + ] + ) + + +@pytest.mark.skip(reason="skip until updated") +def test__all_env_common(env): + return common.test__all_env_common(env) + + +@pytest.mark.skip(reason="skip until updated") +def test__all_env_common(env_with_stoichiometry_sg_check): + return common.test__all_env_common(env_with_stoichiometry_sg_check) From 72eb1fdbc37f2498fc6362b6120b8a7bf798e011 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 12:54:16 -0400 Subject: [PATCH 040/205] Remove test that is already in common. --- tests/gflownet/envs/test_ccrystal.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index b3a0d29bd..657ff544d 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -270,11 +270,6 @@ def test__step__action_sequence_has_expected_result( assert valid == last_action_valid -# TODO: continue from here -def test__get_parents__returns_no_parents_in_initial_state(env): - return common.test__get_parents__returns_no_parents_in_initial_state(env) - - @pytest.mark.skip(reason="skip until updated") @pytest.mark.parametrize( "actions", From 3ad036365ea98932d55f30f86f924175ddece5ba Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 13:29:29 -0400 Subject: [PATCH 041/205] Add common test: forward and backward trajectories are reversible. --- tests/gflownet/envs/common.py | 36 +++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/tests/gflownet/envs/common.py b/tests/gflownet/envs/common.py index 9a3445b57..1bac1ca3a 100644 --- a/tests/gflownet/envs/common.py +++ b/tests/gflownet/envs/common.py @@ -23,6 +23,7 @@ def test__all_env_common(env): test__step_random__does_not_sample_invalid_actions(env) test__forward_actions_have_nonzero_backward_prob(env) test__backward_actions_have_nonzero_forward_prob(env) + test__trajectories_are_reversible(env) test__get_parents_step_get_mask__are_compatible(env) test__sample_backwards_reaches_source(env) test__state2readable__is_reversible(env) @@ -41,6 +42,7 @@ def test__continuous_env_common(env): test__backward_actions_have_nonzero_forward_prob(env) test__step__returns_same_state_action_and_invalid_if_done(env) test__sample_backwards_reaches_source(env) + test__trajectories_are_reversible(env) # test__gflownet_minimal_runs(env) @@ -352,6 +354,40 @@ def test__forward_actions_have_nonzero_backward_prob(env): assert logprobs_bw > -1e6 +@pytest.mark.repeat(1000) +def test__trajectories_are_reversible(env): + env = env.reset() + + # Sample random forward trajectory + states_trajectory_fw = [] + actions_trajectory_fw = [] + while not env.done: + state, action, valid = env.step_random(backward=False) + if valid: + states_trajectory_fw.append(state) + actions_trajectory_fw.append(action) + + # Sample backward trajectory with actions in forward trajectory + states_trajectory_bw = [] + actions_trajectory_bw = [] + actions_trajectory_fw_copy = actions_trajectory_fw.copy() + while not env.equal(env.state, env.source) or env.done: + state, action, valid = env.step_backwards(actions_trajectory_fw_copy.pop()) + if valid: + states_trajectory_bw.append(state) + actions_trajectory_bw.append(action) + + assert all( + [ + env.equal(s_fw, s_bw) + for s_fw, s_bw in zip( + states_trajectory_fw[:-1], states_trajectory_bw[-2::-1] + ) + ] + ) + assert actions_trajectory_fw == actions_trajectory_bw[::-1] + + def test__backward_actions_have_nonzero_forward_prob(env, n=1000): states = _get_terminating_states(env, n) policy_random = torch.unsqueeze( From 060521f8dadb03de4623344c33fc586809b614b2 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 13:48:38 -0400 Subject: [PATCH 042/205] Add self.continuous = True; Update get_parents and add TODO about removing it. --- gflownet/envs/crystals/ccrystal.py | 20 ++++++-------------- 1 file changed, 6 insertions(+), 14 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 36bf65652..86f72d13c 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -58,6 +58,7 @@ def __init__( do_stoichiometry_sg_check: bool = False, **kwargs, ): + self.continuous = True self.composition_kwargs = composition_kwargs or {} self.space_group_kwargs = space_group_kwargs or {} self.lattice_parameters_kwargs = lattice_parameters_kwargs or {} @@ -423,6 +424,7 @@ def _build_state(self, substate: List, stage: Stage) -> List: return output + # TODO: Consider removing altogether def get_parents( self, state: Optional[List] = None, @@ -459,21 +461,11 @@ def get_parents( actions = [self._pad_action(a, Stage.SPACE_GROUP) for a in actions] elif stage == Stage.LATTICE_PARAMETERS: """ - TODO: to be stateless (meaning, operating as a function, not a method with - current object context) this needs to set lattice system based on the - passed state only. Right now it uses the current LatticeParameter - environment, in particular the lattice system that it was set to, and that - changes the invalid actions mask. - - If for some reason a state will be passed to this method that describes an - object with different lattice system than what self.lattice_system contains, - the result will be invalid. + get_parents() is not well defined for continuous environment. Here we + simply return the same state and the representative action. """ - parents, actions = self.lattice_parameters.get_parents( - state=self._get_lattice_parameters_state(state), done=done - ) - parents = [self._build_state(p, Stage.LATTICE_PARAMETERS) for p in parents] - actions = [self._pad_action(a, Stage.LATTICE_PARAMETERS) for a in actions] + parents = [state] + actions = [self.action2representative(action)] else: raise ValueError(f"Unrecognized stage {stage}.") From e8902e25a5bc8040c5f938ed92659ea0a157c2df Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 13:49:10 -0400 Subject: [PATCH 043/205] All tests updated and passed except common. --- tests/gflownet/envs/test_ccrystal.py | 70 +++++++++++++++------------- 1 file changed, 37 insertions(+), 33 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 657ff544d..b202efdfb 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -1,8 +1,9 @@ +import warnings + import common +import numpy as np import pytest import torch -import warnings -import numpy as np from torch import Tensor from gflownet.envs.crystals.ccrystal import CCrystal, Stage @@ -11,9 +12,7 @@ @pytest.fixture def env(): - return CCrystal( - composition_kwargs={"elements": 4} - ) + return CCrystal(composition_kwargs={"elements": 4}) @pytest.fixture @@ -124,7 +123,9 @@ def test__statebatch2proxy__returns_expected_value(env, batch, expected): assert torch.allclose(env.statebatch2proxy(batch), expected, atol=1e-4) -@pytest.mark.parametrize("action", [(1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2)]) +@pytest.mark.parametrize( + "action", [(1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2)] +) def test__step__single_action_works(env, action): env.step(action) @@ -147,7 +148,11 @@ def test__step__single_action_works(env, action): False, ], [ - [(1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), (-1, -1, -2, -2, -2, -2, -2)], + [ + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + ], [1, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.SPACE_GROUP, True, @@ -270,20 +275,17 @@ def test__step__action_sequence_has_expected_result( assert valid == last_action_valid -@pytest.mark.skip(reason="skip until updated") +# TODO: Remove if get_parents is removed @pytest.mark.parametrize( "actions", [ - [(1, 1, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2)], + [(1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2)], [ - (1, 1, -2, -2, -2, -2), - (3, 4, -2, -2, -2, -2), - (-1, -1, -2, -2, -2, -2), - (2, 105, 0, -3, -3, -3), - (-1, -1, -1, -3, -3, -3), - (1, 1, 1, 0, 0, 0), - (1, 1, 0, 0, 0, 0), - (0, 0, 0, 0, 0, 0), + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), ], ], ) @@ -294,19 +296,18 @@ def test__get_parents__contains_previous_action_after_a_step(env, actions): assert action in parent_actions -@pytest.mark.skip(reason="skip until updated") @pytest.mark.parametrize( "actions", [ [ - (1, 1, -2, -2, -2, -2), - (3, 4, -2, -2, -2, -2), - (-1, -1, -2, -2, -2, -2), - (2, 105, 0, -3, -3, -3), - (-1, -1, -1, -3, -3, -3), - (1, 1, 1, 0, 0, 0), - (1, 1, 0, 0, 0, 0), - (0, 0, 0, 0, 0, 0), + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (0.66, 0.55, 0.44, 0.33, 0.22, 0.11, 0), + (np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf), ] ], ) @@ -327,7 +328,6 @@ def test__reset(env, actions): assert env.lattice_parameters.lattice_system == TRICLINIC -@pytest.mark.skip(reason="skip until updated") @pytest.mark.parametrize( "actions, exp_stage", [ @@ -336,16 +336,20 @@ def test__reset(env, actions): Stage.COMPOSITION, ], [ - [(1, 1, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2), (-1, -1, -2, -2, -2, -2)], + [ + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + ], Stage.SPACE_GROUP, ], [ [ - (1, 1, -2, -2, -2, -2), - (3, 4, -2, -2, -2, -2), - (-1, -1, -2, -2, -2, -2), - (2, 105, 0, -3, -3, -3), - (-1, -1, -1, -3, -3, -3), + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), ], Stage.LATTICE_PARAMETERS, ], From 9f3d8bf2bf369528921292c49da1f0bec197eb10 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 14:43:59 -0400 Subject: [PATCH 044/205] Replace test__all_env_common -> test__continuous_env_common --- tests/gflownet/envs/test_ccrystal.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index b202efdfb..5d9b066c7 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -394,8 +394,7 @@ def test__get_mask_invalid_actions_forward__masks_all_actions_from_different_sta ) -@pytest.mark.skip(reason="skip until updated") -def test__all_env_common(env): +def test__continuous_env_common(env): return common.test__all_env_common(env) From eacc12e43a39f8a4d0577f21cb7cef3bd57161c1 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 14:45:22 -0400 Subject: [PATCH 045/205] Implement sample_actions_batch (not tested yet) --- gflownet/envs/crystals/ccrystal.py | 53 ++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 86f72d13c..b8c382d59 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -10,6 +10,7 @@ from gflownet.envs.crystals.clattice_parameters import CLatticeParameters from gflownet.envs.crystals.composition import Composition from gflownet.envs.crystals.spacegroup import SpaceGroup +from gflownet.utils.common import copy, tbool, tfloat, tlong from gflownet.utils.crystals.constants import TRICLINIC @@ -72,6 +73,11 @@ def __init__( self.lattice_parameters = CLatticeParameters( lattice_system=TRICLINIC, **self.lattice_parameters_kwargs ) + self.subenvs = { + Stage.COMPOSITION: self.composition, + Stage.SPACE_GROUP: self.space_group, + Stage.LATTICE_PARAMETERS: self.lattice_parameters, + } # 0-th element of state encodes current stage: 0 for composition, # 1 for space group, 2 for lattice parameters @@ -424,6 +430,53 @@ def _build_state(self, substate: List, stage: Stage) -> List: return output + def sample_actions_batch( + self, + policy_outputs: TensorType["n_states", "policy_output_dim"], + mask: Optional[TensorType["n_states", "policy_output_dim"]] = None, + states_from: List = None, + is_backward: Optional[bool] = False, + sampling_method: Optional[str] = "policy", + temperature_logits: Optional[float] = 1.0, + max_sampling_attempts: Optional[int] = 10, + ) -> Tuple[List[Tuple], TensorType["n_states"]]: + """ + Samples a batch of actions from a batch of policy outputs. + + This method calls the sample_actions_batch() method of the sub-environment + corresponding to each state in the batch. For composition and space_group it + will be the method from the base discrete environment; for the lattice + parameters, it will be the method from the cube environment. + """ + states_dict = {stage: [] for stage in Stage} + stages = [] + for s in states_from: + stage = self.get_stage(s) + states_dict[stage].append(s) + stages.append(stage) + stages_tensor = tlong(stages, device=self.device) + is_subenv_dict = {stage: stages_tensor == stage for stage in Stage} + + # Sample actions from each sub-environment + actions_logprobs_dict = { + stage: subenv.sample_actions_batch( + policy_outputs[is_subenv_dict[stage]], + mask[is_subenv_dict[stage]], + states_dict[stage].values, + sampling_method, + temperature_logits, + max_sampling_attempts, + ) + for stage, subenv in self.subenvs + if torch.any(is_subenv_dict[stage]) + } + + # Stitch all actions in the right order + actions = [] + for stage in stages: + actions.append(actions_logprobs_dict[stage][0].pop(0)) + return actions, _ + # TODO: Consider removing altogether def get_parents( self, From e2fa6d0c894985231916f4184b0139774b9d476f Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 16:13:21 -0400 Subject: [PATCH 046/205] get_policy_output of env base returns torch, not numpy; catch error case --- gflownet/envs/base.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/gflownet/envs/base.py b/gflownet/envs/base.py index cc6c3c3d0..bd96a25da 100644 --- a/gflownet/envs/base.py +++ b/gflownet/envs/base.py @@ -474,6 +474,12 @@ def sample_actions_batch( elif sampling_method == "policy": logits = policy_outputs logits /= temperature_logits + else: + raise NotImplementedError( + f"Sampling method {sampling_method} is invalid. " + "Options are: policy, uniform." + ) + if mask is not None: assert not torch.all(mask), dedent( """ @@ -677,7 +683,9 @@ def get_random_terminating_states( count += 1 return states - def get_policy_output(self, params: Optional[dict] = None): + def get_policy_output( + self, params: Optional[dict] = None + ) -> TensorType["policy_output_dim"]: """ Defines the structure of the output of the policy model, from which an action is to be determined or sampled, by returning a vector with a fixed @@ -686,7 +694,7 @@ def get_policy_output(self, params: Optional[dict] = None): Continuous environments will generally have to overwrite this method. """ - return np.ones(self.action_space_dim) + return torch.ones(self.action_space_dim, dtype=self.float, device=self.device) def state2proxy(self, state: List = None): """ From 12d5794fa1934cc60bd318efe1c1a9abd533f4c2 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 16:13:42 -0400 Subject: [PATCH 047/205] Fixes in sample_actions_batch --- gflownet/envs/crystals/ccrystal.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index b8c382d59..335864381 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -451,31 +451,34 @@ def sample_actions_batch( states_dict = {stage: [] for stage in Stage} stages = [] for s in states_from: - stage = self.get_stage(s) + stage = self._get_stage(s) states_dict[stage].append(s) stages.append(stage) - stages_tensor = tlong(stages, device=self.device) - is_subenv_dict = {stage: stages_tensor == stage for stage in Stage} + stages_tensor = tlong([stage.value for stage in stages], device=self.device) + is_subenv_dict = {stage: stages_tensor == stage.value for stage in Stage} # Sample actions from each sub-environment actions_logprobs_dict = { stage: subenv.sample_actions_batch( policy_outputs[is_subenv_dict[stage]], mask[is_subenv_dict[stage]], - states_dict[stage].values, + states_dict[stage], + is_backward, sampling_method, temperature_logits, max_sampling_attempts, ) - for stage, subenv in self.subenvs + for stage, subenv in self.subenvs.items() if torch.any(is_subenv_dict[stage]) } - # Stitch all actions in the right order + # Stitch all actions in the right order, with the right padding actions = [] for stage in stages: - actions.append(actions_logprobs_dict[stage][0].pop(0)) - return actions, _ + actions.append( + self._pad_action(actions_logprobs_dict[stage][0].pop(0), stage) + ) + return actions, None # TODO: Consider removing altogether def get_parents( From 3474352278306321fd7011fac9d1cfe5ed9da114 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 16:14:44 -0400 Subject: [PATCH 048/205] Tests for sample_actions_batch --- tests/gflownet/envs/test_ccrystal.py | 55 ++++++++++++++++++++++++++++ 1 file changed, 55 insertions(+) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 5d9b066c7..d4da3e8f4 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -9,6 +9,8 @@ from gflownet.envs.crystals.ccrystal import CCrystal, Stage from gflownet.envs.crystals.clattice_parameters import TRICLINIC +from gflownet.utils.common import tbool, tfloat + @pytest.fixture def env(): @@ -393,7 +395,60 @@ def test__get_mask_invalid_actions_forward__masks_all_actions_from_different_sta ] ) +@pytest.mark.skip(reason="skip while developping other tests") +@pytest.mark.repeat(10) +def test__step_random__does_not_crash_from_source(env): + """ + Very low bar test... + """ + env.reset() + env.step_random() + pass +@pytest.mark.parametrize( + "states", + [ + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + ], + ], +) +def test__sample_actions_forward__returns_valid_actions(env, states): + """ + Still low bar, but getting better... + """ + n_states = len(states) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device + ) + # Build policy outputs + params = env.random_distr_params + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Sample actions + actions, _ = env.sample_actions_batch( + policy_outputs, masks, states, is_backward=False + ) + # Sample actions are valid + for state, action in zip(states, actions): + assert action in env.get_valid_actions(state, done=False, backward=False) + + + +@pytest.mark.skip(reason="skip until updated") def test__continuous_env_common(env): return common.test__all_env_common(env) From b21f7ad8f6182c86c2675120803c60b08048c264 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 22:23:33 -0400 Subject: [PATCH 049/205] Add self.mask_dim to environments. --- gflownet/envs/base.py | 1 + gflownet/envs/ctorus.py | 2 ++ gflownet/envs/cube.py | 31 +++++++++++++++++-------------- 3 files changed, 20 insertions(+), 14 deletions(-) diff --git a/gflownet/envs/base.py b/gflownet/envs/base.py index bd96a25da..6fe3421b0 100644 --- a/gflownet/envs/base.py +++ b/gflownet/envs/base.py @@ -90,6 +90,7 @@ def __init__( self.action_space, device=self.device, dtype=self.float ) self.action_space_dim = len(self.action_space) + self.mask_dim = self.action_space_dim # Max trajectory length self.max_traj_length = self.get_max_traj_length() # Policy outputs diff --git a/gflownet/envs/ctorus.py b/gflownet/envs/ctorus.py index 3cf8543bd..816d28f3c 100644 --- a/gflownet/envs/ctorus.py +++ b/gflownet/envs/ctorus.py @@ -35,6 +35,8 @@ class ContinuousTorus(HybridTorus): def __init__(self, **kwargs): super().__init__(**kwargs) + # Mask dimensionality: + self.mask_dim = 2 def get_action_space(self): """ diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 84a5f2888..e239846e0 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -332,6 +332,9 @@ class ContinuousCube(CubeBase): def __init__(self, **kwargs): super().__init__(**kwargs) + # Mask dimensionality: 3 + number of dimensions + self.mask_dim_base = 3 + self.mask_dim = self.mask_dim_base + self.n_dim def get_action_space(self): """ @@ -357,10 +360,13 @@ def get_max_traj_length(self): def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: """ - Defines the structure of the output of the policy model, from which an - action is to be determined or sampled, by returning a vector with a fixed - random policy. The environment consists of both continuous and discrete - actions. + Defines the structure of the output of the policy model. + + The policy output will be used to initialize a distribution, from which an + action is to be determined or sampled. This method returns a vector with a + fixed policy defined by params. + + The environment consists of both continuous and discrete actions. Continuous actions @@ -509,13 +515,11 @@ def get_mask_invalid_actions_forward( """ state = self._get_state(state) done = self._get_done(done) - mask_dim_base = 3 - mask_dim = mask_dim_base + self.n_dim # If done, the entire mask is True (all actions are "invalid" and no special # cases) if done: - return [True] * mask_dim - mask = [False] * mask_dim_base + self.ignored_dims + return [True] * self.mask_dim + mask = [False] * self.mask_dim_base + self.ignored_dims # If the state is the source state, EOS is invalid if self._get_effective_dims(state) == self._get_effective_dims(self.source): mask[2] = True @@ -555,8 +559,7 @@ def get_mask_invalid_actions_backward(self, state=None, done=None, parents_a=Non """ state = self._get_state(state) done = self._get_done(done) - mask_dim_base = 3 - mask = [True] * mask_dim_base + self.ignored_dims + mask = [True] * self.mask_dim_base + self.ignored_dims # If the state is the source state, entire mask is True if self._get_effective_dims(state) == self._get_effective_dims(self.source): return mask @@ -727,7 +730,7 @@ def _mask_ignored_dimensions( def sample_actions_batch( self, policy_outputs: TensorType["n_states", "policy_output_dim"], - mask: Optional[TensorType["n_states", "policy_output_dim"]] = None, + mask: Optional[TensorType["n_states", "mask_dim"]] = None, states_from: List = None, is_backward: Optional[bool] = False, sampling_method: Optional[str] = "policy", @@ -749,7 +752,7 @@ def sample_actions_batch( def _sample_actions_batch_forward( self, policy_outputs: TensorType["n_states", "policy_output_dim"], - mask: Optional[TensorType["n_states", "policy_output_dim"]] = None, + mask: Optional[TensorType["n_states", "mask_dim"]] = None, states_from: List = None, sampling_method: Optional[str] = "policy", temperature_logits: Optional[float] = 1.0, @@ -854,7 +857,7 @@ def _sample_actions_batch_forward( def _sample_actions_batch_backward( self, policy_outputs: TensorType["n_states", "policy_output_dim"], - mask: Optional[TensorType["n_states", "policy_output_dim"]] = None, + mask: Optional[TensorType["n_states", "mask_dim"]] = None, states_from: List = None, sampling_method: Optional[str] = "policy", temperature_logits: Optional[float] = 1.0, @@ -957,7 +960,7 @@ def get_logprobs( self, policy_outputs: TensorType["n_states", "policy_output_dim"], actions: TensorType["n_states", "actions_dim"], - mask: TensorType["n_states", "3"], + mask: TensorType["n_states", "mask_dim"], states_from: List, is_backward: bool, ) -> TensorType["batch_size"]: From 6c3b1ba5498ae29798eec555c12c9a49c44976e0 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 23 Sep 2023 22:24:28 -0400 Subject: [PATCH 050/205] Significant progress in continuous crystal but not there yet. --- gflownet/envs/crystals/ccrystal.py | 207 ++++++++++++++++++----- tests/gflownet/envs/test_ccrystal.py | 241 ++++++++++++++++++++++++++- 2 files changed, 396 insertions(+), 52 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 335864381..0e9e3346c 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -59,7 +59,6 @@ def __init__( do_stoichiometry_sg_check: bool = False, **kwargs, ): - self.continuous = True self.composition_kwargs = composition_kwargs or {} self.space_group_kwargs = space_group_kwargs or {} self.lattice_parameters_kwargs = lattice_parameters_kwargs or {} @@ -136,12 +135,23 @@ def __init__( self.lattice_parameters.eos, Stage.LATTICE_PARAMETERS ) + # Mask dimensionality + self.mask_dim = sum([subenv.mask_dim for subenv in self.subenvs.values()]) + # Conversions self.state2proxy = self.state2oracle self.statebatch2proxy = self.statebatch2oracle self.statetorch2proxy = self.statetorch2oracle - super().__init__(**kwargs) + # Base class init + # Since only the lattice parameters subenv has distribution parameters, only + # these are pased to the base init. + super().__init__( + fixed_distr_params=self.lattice_parameters.fixed_distr_params, + random_distr_params=self.lattice_parameters.random_distr_params, + **kwargs, + ) + self.continuous = True def _set_lattice_parameters(self): """ @@ -232,6 +242,65 @@ def get_max_traj_length(self) -> int: + self.lattice_parameters.get_max_traj_length() ) + def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: + """ + Defines the structure of the output of the policy model. + + The policy output is in this case the concatenation of the policy outputs of + the three sub-environments. + """ + return torch.cat( + [subenv.get_policy_output(params) for subenv in self.subenvs.values()] + ) + + def _get_policy_outputs_of_subenv( + self, policy_outputs: TensorType["n_states", "policy_output_dim"], stage: Stage + ): + """ + Returns the columns of the policy outputs that correspond to the + sub-environment indicated by stage. + + Args + ---- + policy_outputs : tensor + A tensor containing a batch of policy outputs. It is assumed that all the + rows in the this tensor correspond to the same stage. + + stage : Stage + Identifier of the sub-environment of which the corresponding columns of the + policy outputs are to be extracted. + """ + init_col = 0 + for stg, subenv in self.subenvs.items(): + end_col = init_col + subenv.policy_output_dim + if stg == stage: + return policy_outputs[:, init_col:end_col] + init_col = end_col + + def _get_mask_of_subenv( + self, mask: TensorType["n_states", "mask_dim"], stage: Stage + ): + """ + Returns the columns of a tensor of masks that correspond to the sub-environment + indicated by stage. + + Args + ---- + mask : tensor + A tensor containing a batch of masks. It is assumed that all the rows in + the this tensor correspond to the same stage. + + stage : Stage + Identifier of the sub-environment of which the corresponding columns of the + masks are to be extracted. + """ + init_col = 0 + for stg, subenv in self.subenvs.items(): + end_col = init_col + subenv.mask_dim + if stg == stage: + return mask[:, init_col:end_col] + init_col = end_col + def reset(self, env_id: Union[int, str] = None): self.composition.reset() self.space_group.reset() @@ -262,6 +331,50 @@ def _set_stage(self, stage: Stage, state: Optional[List] = None): state = self.state state[0] = stage.value + def _get_policy_states_of_subenv( + self, state: TensorType["n_states", "state_dim"], stage: Stage + ): + """ + Returns the part of the states corresponding to the subenv indicated by stage. + + Args + ---- + states : tensor + A tensor containing a batch of states in policy format. + + stage : Stage + Identifier of the sub-environment of which the corresponding columns of the + batch of states are to be extracted. + """ + init_col = 0 + for stg, subenv in self.subenvs.items(): + end_col = init_col + subenv.policy_input_dim + if stg == stage: + return states[:, init_col:end_col] + init_col = end_col + + def _get_state_of_subenv(self, state: List, stage: Optional[Stage] = None): + """ + Returns the part of the state corresponding to the subenv indicated by stage. + + Args + ---- + state : list + A state of the parent Crystal environment. + + stage : Stage + Identifier of the sub-environment of which the corresponding part of the + state is to be extracted. If None, it is inferred from the state. + """ + if stage is None: + stage = self._get_stage(state) + init_col = 1 + for stg, subenv in self.subenvs.items(): + end_col = init_col + len(subenv.source) + if stg == stage: + return state[init_col:end_col] + init_col = end_col + def _get_composition_state(self, state: Optional[List[int]] = None) -> List[int]: state = self._get_state(state) @@ -301,54 +414,45 @@ def _get_lattice_parameters_tensor_states( def get_mask_invalid_actions_forward( self, state: Optional[List[int]] = None, done: Optional[bool] = None ) -> List[bool]: + """ + Computes the forward actions mask of the state. + + The mask of the parent crystal is simply the concatenation of the masks of the + three sub-environments. This assumes that the methods that will use the mask + will extract the part corresponding to the relevant stage and ignore the rest. + """ state = self._get_state(state) done = self._get_done(done) - stage = self._get_stage(state) - if done: - return [True] * self.action_space_dim + mask = [] + for stage, subenv in self.subenvs.items(): + mask.extend( + subenv.get_mask_invalid_actions_forward( + self._get_state_of_subenv(state, stage), done + ) + ) + return mask - mask = [True] * self.action_space_dim + def get_mask_invalid_actions_backward( + self, state: Optional[List[int]] = None, done: Optional[bool] = None + ) -> List[bool]: + """ + Computes the backward actions mask of the state. - if stage == Stage.COMPOSITION: - composition_mask = self.composition.get_mask_invalid_actions_forward( - state=self._get_composition_state(state), done=False - ) - mask[ - self.composition_mask_start : self.composition_mask_end - ] = composition_mask - elif stage == Stage.SPACE_GROUP: - space_group_state = self._get_space_group_state(state) - space_group_mask = self.space_group.get_mask_invalid_actions_forward( - state=space_group_state, done=False - ) - mask[ - self.space_group_mask_start : self.space_group_mask_end - ] = space_group_mask - elif stage == Stage.LATTICE_PARAMETERS: - """ - TODO: to be stateless (meaning, operating as a function, not a method with - current object context) this needs to set lattice system based on the - passed state only. Right now it uses the current LatticeParameter - environment, in particular the lattice system that it was set to, and that - changes the invalid actions mask. - - If for some reason a state will be passed to this method that describes an - object with different lattice system than what self.lattice_system - contains, the result will be invalid. - """ - lattice_parameters_state = self._get_lattice_parameters_state(state) - lattice_parameters_mask = ( - self.lattice_parameters.get_mask_invalid_actions_forward( - state=lattice_parameters_state, done=False + The mask of the parent crystal is simply the concatenation of the masks of the + three sub-environments. This assumes that the methods that will use the mask + will extract the part corresponding to the relevant stage and ignore the rest. + """ + state = self._get_state(state) + done = self._get_done(done) + + mask = [] + for stage, subenv in self.subenvs.items(): + mask.extend( + subenv.get_mask_invalid_actions_backward( + self._get_state_of_subenv(state, stage), done ) ) - mask[ - self.lattice_parameters_mask_start : self.lattice_parameters_mask_end - ] = lattice_parameters_mask - else: - raise ValueError(f"Unrecognized stage {stage}.") - return mask def _update_state(self): @@ -447,12 +551,21 @@ def sample_actions_batch( corresponding to each state in the batch. For composition and space_group it will be the method from the base discrete environment; for the lattice parameters, it will be the method from the cube environment. + + Note that in order to call sample_actions_batch() of the sub-environments, we + need to first extract the part of the policy outputs, the masks and the states + that correspond to the sub-environment. """ states_dict = {stage: [] for stage in Stage} + """ + A dictionary with keys equal to Stage and the values are the list of states in + the stage of the key. The states are only the part corresponding to the + sub-environment. + """ stages = [] for s in states_from: stage = self._get_stage(s) - states_dict[stage].append(s) + states_dict[stage].append(self._get_state_of_subenv(s, stage)) stages.append(stage) stages_tensor = tlong([stage.value for stage in stages], device=self.device) is_subenv_dict = {stage: stages_tensor == stage.value for stage in Stage} @@ -460,8 +573,10 @@ def sample_actions_batch( # Sample actions from each sub-environment actions_logprobs_dict = { stage: subenv.sample_actions_batch( - policy_outputs[is_subenv_dict[stage]], - mask[is_subenv_dict[stage]], + self._get_policy_outputs_of_subenv( + policy_outputs[is_subenv_dict[stage]], stage + ), + self._get_mask_of_subenv(mask[is_subenv_dict[stage]], stage), states_dict[stage], is_backward, sampling_method, diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index d4da3e8f4..c80708f17 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -8,13 +8,15 @@ from gflownet.envs.crystals.ccrystal import CCrystal, Stage from gflownet.envs.crystals.clattice_parameters import TRICLINIC - from gflownet.utils.common import tbool, tfloat @pytest.fixture def env(): - return CCrystal(composition_kwargs={"elements": 4}) + return CCrystal( + composition_kwargs={"elements": 4}, + space_group_kwargs={"space_groups_subset": list(range(1, 15 + 1)) + [105]}, + ) @pytest.fixture @@ -144,7 +146,7 @@ def test__step__single_action_works(env, action): True, ], [ - [(2, 225, 3, -3, -3, -3, -3)], + [(2, 105, 3, -3, -3, -3, -3)], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.COMPOSITION, False, @@ -330,6 +332,7 @@ def test__reset(env, actions): assert env.lattice_parameters.lattice_system == TRICLINIC +@pytest.mark.skip(reason="skip while developping other tests") @pytest.mark.parametrize( "actions, exp_stage", [ @@ -395,7 +398,205 @@ def test__get_mask_invalid_actions_forward__masks_all_actions_from_different_sta ] ) -@pytest.mark.skip(reason="skip while developping other tests") + +def test__get_policy_outputs__is_the_concatenation_of_subenvs(env): + policy_output_composition = env.composition.get_policy_output( + env.composition.fixed_distr_params + ) + policy_output_space_group = env.space_group.get_policy_output( + env.space_group.fixed_distr_params + ) + policy_output_lattice_parameters = env.lattice_parameters.get_policy_output( + env.lattice_parameters.fixed_distr_params + ) + policy_output_cat = torch.cat( + ( + policy_output_composition, + policy_output_space_group, + policy_output_lattice_parameters, + ) + ) + policy_output = env.get_policy_output(env.fixed_distr_params) + assert torch.all(torch.eq(policy_output_cat, policy_output)) + + +def test___get_policy_outputs_of_subenv__returns_correct_output(env): + n_states = 5 + policy_output_composition = torch.tile( + env.composition.get_policy_output(env.composition.fixed_distr_params), + dims=(n_states, 1), + ) + policy_output_space_group = torch.tile( + env.space_group.get_policy_output(env.space_group.fixed_distr_params), + dims=(n_states, 1), + ) + policy_output_lattice_parameters = torch.tile( + env.lattice_parameters.get_policy_output( + env.lattice_parameters.fixed_distr_params + ), + dims=(n_states, 1), + ) + policy_outputs = torch.tile( + env.get_policy_output(env.fixed_distr_params), dims=(n_states, 1) + ) + assert torch.all( + torch.eq( + env._get_policy_outputs_of_subenv(policy_outputs, Stage.COMPOSITION), + policy_output_composition, + ) + ) + assert torch.all( + torch.eq( + env._get_policy_outputs_of_subenv(policy_outputs, Stage.SPACE_GROUP), + policy_output_space_group, + ) + ) + assert torch.all( + torch.eq( + env._get_policy_outputs_of_subenv(policy_outputs, Stage.LATTICE_PARAMETERS), + policy_output_lattice_parameters, + ) + ) + + +@pytest.mark.parametrize( + "state, state_composition, state_space_group, state_lattice_parameters", + [ + [ + [0, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [0, 0, 0], + [-1, -1, -1, -1, -1, -1], + ], + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 0, 0], + [0, 0, 0], + [-1, -1, -1, -1, -1, -1], + ], + [ + [1, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [0, 0, 0], + [-1, -1, -1, -1, -1, -1], + ], + [ + [1, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [4, 3, 105], + [-1, -1, -1, -1, -1, -1], + ], + [ + [1, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [4, 3, 105], + [-1, -1, -1, -1, -1, -1], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [4, 3, 105], + [-1, -1, -1, -1, -1, -1], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [4, 3, 105], + [-1, -1, -1, -1, -1, -1], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [1, 0, 4, 0], + [4, 3, 105], + [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [1, 0, 4, 0], + [4, 3, 105], + [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [1, 0, 4, 0], + [4, 3, 105], + [0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [1, 0, 4, 0], + [4, 3, 105], + [0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + ], + ], +) +def test__state_of_subenv__returns_expected( + env, state, state_composition, state_space_group, state_lattice_parameters +): + for stage in Stage: + state_subenv = env._get_state_of_subenv(state, stage) + if stage == Stage.COMPOSITION: + assert state_subenv == state_composition + elif stage == Stage.SPACE_GROUP: + assert state_subenv == state_space_group + elif stage == Stage.LATTICE_PARAMETERS: + assert state_subenv == state_lattice_parameters + else: + raise ValueError(f"Unrecognized stage {stage}.") + + +@pytest.mark.parametrize( + "states", + [ + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.12, 0.23, 0.34, 0.45, 0.56, 0.67], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + ], +) +def test__get_mask_of_subenv__returns_correct_submasks(env, states): + # Get states from each stage and masks computed with the Crystal env. + states_dict = {stage: [] for stage in Stage} + masks_dict = {stage: [] for stage in Stage} + stages = [] + for s in states: + stage = env._get_stage(s) + states_dict[stage].append(s) + masks_dict[stage].append(env.get_mask_invalid_actions_forward(s)) + stages.append(stage) + + for stage, subenv in env.subenvs.items(): + # Get masks computed with subenv + masks_subenv = tbool( + [ + subenv.get_mask_invalid_actions_forward( + env._get_state_of_subenv(s, stage) + ) + for s in states_dict[stage] + ], + device=env.device, + ) + assert torch.all( + torch.eq( + env._get_mask_of_subenv( + tbool(masks_dict[stage], device=env.device), stage + ), + masks_subenv, + ) + ) + + @pytest.mark.repeat(10) def test__step_random__does_not_crash_from_source(env): """ @@ -405,6 +606,8 @@ def test__step_random__does_not_crash_from_source(env): env.step_random() pass + +# @pytest.mark.skip(reason="skip while developping other tests") @pytest.mark.parametrize( "states", [ @@ -418,11 +621,24 @@ def test__step_random__does_not_crash_from_source(env): ], [ [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + ], + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.12, 0.23, 0.34, 0.45, 0.56, 0.67], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], ], ], ) @@ -432,6 +648,7 @@ def test__sample_actions_forward__returns_valid_actions(env, states): """ n_states = len(states) # Get masks + lens = [len(env.get_mask_invalid_actions_forward(s)) for s in states] masks = tbool( [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device ) @@ -444,9 +661,21 @@ def test__sample_actions_forward__returns_valid_actions(env, states): ) # Sample actions are valid for state, action in zip(states, actions): + if env._get_stage(state) == Stage.LATTICE_PARAMETERS: + continue assert action in env.get_valid_actions(state, done=False, backward=False) +@pytest.mark.skip(reason="gets stuck") +@pytest.mark.repeat(10) +def test__trajectory_random__does_not_crash_from_source(env): + """ + Raising the bar... + """ + env.reset() + env.trajectory_random() + pass + @pytest.mark.skip(reason="skip until updated") def test__continuous_env_common(env): From 76589e87fffb8ce86f9016e1e6cef51aaa204aa6 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 00:29:33 -0400 Subject: [PATCH 051/205] Minor change --- gflownet/envs/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/envs/base.py b/gflownet/envs/base.py index 6fe3421b0..06b401091 100644 --- a/gflownet/envs/base.py +++ b/gflownet/envs/base.py @@ -628,7 +628,7 @@ def trajectory_random(self): The list of actions (tuples) in the trajectory. """ actions = [] - while self.done is not True: + while not self.done: _, action, valid = self.step_random() if valid: actions.append(action) From e9c53412b2bdfa40b8a312cffbda5d77569d1c73 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 00:30:17 -0400 Subject: [PATCH 052/205] Check only whether effective dimensions are within bounds. --- gflownet/envs/cube.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index e239846e0..3f4e89291 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -1265,7 +1265,10 @@ def _step( state[dim] += incr # If state is out of bounds, return invalid - if any([s > 1.0 for s in state]) or any([s < 0.0 for s in state]): + effective_dims = self._get_effective_dims(state) + if any([s > 1.0 for s in effective_dims]) or any( + [s < 0.0 for s in effective_dims] + ): warnings.warn( f""" State is out of cube bounds. From bdbc4af9c7d77ebad49d12dbfd06d6ee12d28767 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 00:31:25 -0400 Subject: [PATCH 053/205] Changes in step() --- gflownet/envs/crystals/ccrystal.py | 49 +++++++++++++++--------------- 1 file changed, 24 insertions(+), 25 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 0e9e3346c..332752381 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -455,20 +455,13 @@ def get_mask_invalid_actions_backward( ) return mask - def _update_state(self): - """ - Updates current state based on the states of underlying environments. - """ - self.state = ( - [self._get_stage(self.state).value] - + self.composition.state - + self.space_group.state - + self.lattice_parameters.state - ) - def step( self, action: Tuple[int], skip_mask_check: bool = False ) -> Tuple[List[int], Tuple[int], bool]: + stage = self._get_stage(self.state) + # Skip mask check if stage is lattice parameters (continuous actions) + if stage == Stage.LATTICE_PARAMETERS: + skip_mask_check = True # Replace action by its representative to check against the mask. action_to_check = self.action2representative(action) do_step, self.state, action_to_check = self._pre_step( @@ -478,37 +471,43 @@ def step( if not do_step: return self.state, action, False - self.n_actions += 1 - stage = self._get_stage(self.state) if stage == Stage.COMPOSITION: - composition_action = self._depad_action(action, Stage.COMPOSITION) - _, executed_action, valid = self.composition.step(composition_action) - if valid and executed_action == self.composition.eos: + action_composition = self._depad_action(action, Stage.COMPOSITION) + _, action_composition, valid = self.composition.step(action_composition) + if valid and action_composition == self.composition.eos: self._set_stage(Stage.SPACE_GROUP) if self.do_stoichiometry_sg_check: self.space_group.set_n_atoms_compatibility_dict( self.composition.state ) elif stage == Stage.SPACE_GROUP: - stage_group_action = self._depad_action(action, Stage.SPACE_GROUP) - _, executed_action, valid = self.space_group.step(stage_group_action) - if valid and executed_action == self.space_group.eos: + action_space_group = self._depad_action(action, Stage.SPACE_GROUP) + _, action_space_group, valid = self.space_group.step(action_space_group) + if valid and action_space_group == self.space_group.eos: self._set_stage(Stage.LATTICE_PARAMETERS) self._set_lattice_parameters() elif stage == Stage.LATTICE_PARAMETERS: - lattice_parameters_action = self._depad_action( + action_lattice_parameters = self._depad_action( action, Stage.LATTICE_PARAMETERS ) - _, executed_action, valid = self.lattice_parameters.step( - lattice_parameters_action + _, action_lattice_parameters, valid = self.lattice_parameters.step( + action_lattice_parameters ) - if valid and executed_action == self.lattice_parameters.eos: + if valid and action_lattice_parameters == self.lattice_parameters.eos: + self.n_actions += 1 self.done = True + return self.state, self.eos, True else: raise ValueError(f"Unrecognized stage {stage}.") - self._update_state() - + if valid: + self.n_actions += 1 + self.state = ( + [self._get_stage(self.state).value] + + self.composition.state + + self.space_group.state + + self.lattice_parameters.state + ) return self.state, action, valid def _build_state(self, substate: List, stage: Stage) -> List: From 4e89028b17684b51332f2aab4853c55acc20158f Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 00:31:57 -0400 Subject: [PATCH 054/205] Re-enable test --- tests/gflownet/envs/test_ccrystal.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index c80708f17..0acb5aad9 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -666,8 +666,7 @@ def test__sample_actions_forward__returns_valid_actions(env, states): assert action in env.get_valid_actions(state, done=False, backward=False) -@pytest.mark.skip(reason="gets stuck") -@pytest.mark.repeat(10) +@pytest.mark.repeat(100) def test__trajectory_random__does_not_crash_from_source(env): """ Raising the bar... From d64cb1395c75a9f8058cd2ec17df1c6c09ffbd4d Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 01:36:52 -0400 Subject: [PATCH 055/205] Remove old test_cube.py --- tests/gflownet/envs/test_cube.py | 97 -------------------------------- 1 file changed, 97 deletions(-) delete mode 100644 tests/gflownet/envs/test_cube.py diff --git a/tests/gflownet/envs/test_cube.py b/tests/gflownet/envs/test_cube.py deleted file mode 100644 index df7812cd8..000000000 --- a/tests/gflownet/envs/test_cube.py +++ /dev/null @@ -1,97 +0,0 @@ -import common -import numpy as np -import pytest -import torch - -from gflownet.envs.cube import HybridCube - - -@pytest.fixture -def env(): - return HybridCube(n_dim=2, n_comp=3) - - -@pytest.mark.parametrize( - "action_space", - [ - [ - (0, 0.0), - (1, 0.0), - (2, 0.0), - ], - ], -) -def test__get_action_space__returns_expected(env, action_space): - assert set(action_space) == set(env.action_space) - - -def test__get_policy_output__returns_expected(env): - assert env.policy_output_dim == env.n_dim * env.n_comp * 3 + env.n_dim + 1 - fixed_policy_output = env.fixed_policy_output - random_policy_output = env.random_policy_output - assert torch.all(fixed_policy_output[: env.n_dim + 1] == 1) - assert torch.all(random_policy_output[: env.n_dim + 1] == 1) - assert torch.all(fixed_policy_output[env.n_dim + 1 :: 3] == 1) - assert torch.all( - fixed_policy_output[env.n_dim + 2 :: 3] == env.fixed_distr_params["beta_alpha"] - ) - assert torch.all( - fixed_policy_output[env.n_dim + 3 :: 3] == env.fixed_distr_params["beta_beta"] - ) - assert torch.all(random_policy_output[env.n_dim + 1 :: 3] == 1) - assert torch.all( - random_policy_output[env.n_dim + 2 :: 3] - == env.random_distr_params["beta_alpha"] - ) - assert torch.all( - random_policy_output[env.n_dim + 3 :: 3] == env.random_distr_params["beta_beta"] - ) - - -@pytest.mark.parametrize( - "state, expected", - [ - ( - [0.0, 0.0], - [0.0, 0.0], - ), - ( - [1.0, 1.0], - [1.0, 1.0], - ), - ( - [1.1, 1.00001], - [1.0, 1.0], - ), - ( - [-0.1, 1.00001], - [0.0, 1.0], - ), - ( - [0.1, 0.21], - [0.1, 0.21], - ), - ], -) -def test__state2policy_returns_expected(env, state, expected): - assert env.state2policy(state) == expected - - -@pytest.mark.parametrize( - "states, expected", - [ - ( - [[0.0, 0.0], [1.0, 1.0], [1.1, 1.00001], [-0.1, 1.00001], [0.1, 0.21]], - [[0.0, 0.0], [1.0, 1.0], [1.0, 1.0], [0.0, 1.0], [0.1, 0.21]], - ), - ], -) -def test__statebatch_torch2policy_returns_expected(env, states, expected): - assert np.equal(env.statebatch2policy(states), np.array(expected)).all() - assert torch.equal( - env.statetorch2policy(torch.tensor(states)), torch.tensor(expected) - ) - - -# def test__continuous_env_common(env): -# return common.test__continuous_env_common(env) From 8c1921f3bdda6a636bb0891182a9831652576395 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 11:25:04 -0400 Subject: [PATCH 056/205] Add Stage DONE --- gflownet/envs/crystals/ccrystal.py | 1 + tests/gflownet/envs/test_ccrystal.py | 8 ++------ 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 332752381..975cd23bb 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -24,6 +24,7 @@ class Stage(Enum): COMPOSITION = 0 SPACE_GROUP = 1 LATTICE_PARAMETERS = 2 + DONE = 3 def to_pad(self) -> int: """ diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 0acb5aad9..6396cc25e 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -57,11 +57,7 @@ def test__environment__has_expected_action_space(env): def test__pad_depad_action(env): - for subenv, stage in [ - (env.composition, Stage.COMPOSITION), - (env.space_group, Stage.SPACE_GROUP), - (env.lattice_parameters, Stage.LATTICE_PARAMETERS), - ]: + for stage, subenv in env.subenvs.items(): for action in subenv.action_space: padded = env._pad_action(action, stage) assert len(padded) == env.max_action_length @@ -533,7 +529,7 @@ def test___get_policy_outputs_of_subenv__returns_correct_output(env): def test__state_of_subenv__returns_expected( env, state, state_composition, state_space_group, state_lattice_parameters ): - for stage in Stage: + for stage in env.subenvs: state_subenv = env._get_state_of_subenv(state, stage) if stage == Stage.COMPOSITION: assert state_subenv == state_composition From ed4df708310bebab14edc71633ce76385ec8de4d Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 11:32:15 -0400 Subject: [PATCH 057/205] Add next() to Stage --- gflownet/envs/crystals/ccrystal.py | 8 ++++++++ tests/gflownet/envs/test_ccrystal.py | 7 +++++++ 2 files changed, 15 insertions(+) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 975cd23bb..90861b425 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -26,6 +26,14 @@ class Stage(Enum): LATTICE_PARAMETERS = 2 DONE = 3 + def next(self) -> "Stage": + """ + Returns the next Stage in the enumeration or None if at the last stage. + """ + if self.value + 1 == len(Stage): + return None + return Stage(self.value + 1) + def to_pad(self) -> int: """ Maps stage value to a padding. The following mapping is used: diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 6396cc25e..3f2769b17 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -27,6 +27,13 @@ def env_with_stoichiometry_sg_check(): ) +def test__stage_next__returns_expected(): + assert Stage.next(Stage.COMPOSITION) == Stage.SPACE_GROUP + assert Stage.next(Stage.SPACE_GROUP) == Stage.LATTICE_PARAMETERS + assert Stage.next(Stage.LATTICE_PARAMETERS) == Stage.DONE + assert Stage.next(Stage.DONE) == None + + def test__environment__initializes_properly(env): pass From fb25f42559a957349d44cfe9670babb43ea07ca6 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 11:54:30 -0400 Subject: [PATCH 058/205] Simplify step() --- gflownet/envs/crystals/ccrystal.py | 41 ++++++++++++++---------------- 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 90861b425..3e5d4cbd1 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -177,6 +177,7 @@ def _set_lattice_parameters(self): lattice_system=self.space_group.lattice_system, **self.lattice_parameters_kwargs, ) + self.subenvs[Stage.LATTICE_PARAMETERS] = self.lattice_parameters def _pad_action(self, action: Tuple[int], stage: Stage) -> Tuple[int]: """ @@ -480,39 +481,35 @@ def step( if not do_step: return self.state, action, False - if stage == Stage.COMPOSITION: - action_composition = self._depad_action(action, Stage.COMPOSITION) - _, action_composition, valid = self.composition.step(action_composition) - if valid and action_composition == self.composition.eos: - self._set_stage(Stage.SPACE_GROUP) + # Call step of current subenvironment + action_subenv = self._depad_action(action, stage) + _, action_subenv, valid = self.subenvs[stage].step(action_subenv) + + # If action is invalid, exit immediately. Otherwise increment actions and go on + if not valid: + return self.state, action, False + self.n_actions += 1 + + # If action is EOS of subenv, advance stage and set constraints or exit + if action_subenv == self.subenvs[stage].eos: + stage = Stage.next(stage) + if stage == Stage.SPACE_GROUP: if self.do_stoichiometry_sg_check: self.space_group.set_n_atoms_compatibility_dict( self.composition.state ) - elif stage == Stage.SPACE_GROUP: - action_space_group = self._depad_action(action, Stage.SPACE_GROUP) - _, action_space_group, valid = self.space_group.step(action_space_group) - if valid and action_space_group == self.space_group.eos: - self._set_stage(Stage.LATTICE_PARAMETERS) + elif stage == Stage.LATTICE_PARAMETERS: self._set_lattice_parameters() - elif stage == Stage.LATTICE_PARAMETERS: - action_lattice_parameters = self._depad_action( - action, Stage.LATTICE_PARAMETERS - ) - _, action_lattice_parameters, valid = self.lattice_parameters.step( - action_lattice_parameters - ) - if valid and action_lattice_parameters == self.lattice_parameters.eos: + elif stage == Stage.DONE: self.n_actions += 1 self.done = True return self.state, self.eos, True - else: - raise ValueError(f"Unrecognized stage {stage}.") + else: + raise ValueError(f"Unrecognized stage {stage}.") if valid: - self.n_actions += 1 self.state = ( - [self._get_stage(self.state).value] + [stage.value] + self.composition.state + self.space_group.state + self.lattice_parameters.state From d96aef57470acec1954175fdee7a2bfed98ad5b7 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 11:58:23 -0400 Subject: [PATCH 059/205] Add docstring --- gflownet/envs/crystals/ccrystal.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 3e5d4cbd1..acc8e715a 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -468,6 +468,30 @@ def get_mask_invalid_actions_backward( def step( self, action: Tuple[int], skip_mask_check: bool = False ) -> Tuple[List[int], Tuple[int], bool]: + """ + Executes forward step given an action. + + The action is performed by the corresponding sub-environment and then the + global state is updated accordingly. If the action is the EOS of the + sub-environment, the stage is advanced and constraints are set on the + subsequent sub-environment. + + Args + ---- + action : tuple + Action to be executed. The input action is global, that is padded. + + Returns + ------- + self.state : list + The state after executing the action. + + action : int + Action executed. + + valid : bool + False, if the action is not allowed for the current state. True otherwise. + """ stage = self._get_stage(self.state) # Skip mask check if stage is lattice parameters (continuous actions) if stage == Stage.LATTICE_PARAMETERS: From 4b30cba99c1ee633f5226831fe31a952420d7820 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 12:13:54 -0400 Subject: [PATCH 060/205] Implement step_backwards. Test to be added. --- gflownet/envs/crystals/ccrystal.py | 86 +++++++++++++++++++++++++--- tests/gflownet/envs/test_ccrystal.py | 7 +++ 2 files changed, 85 insertions(+), 8 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index acc8e715a..0f6b6f9c8 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -34,6 +34,14 @@ def next(self) -> "Stage": return None return Stage(self.value + 1) + def prev(self) -> "Stage": + """ + Returns the previous Stage in the enumeration or None if at the first stage. + """ + if self.value - 1 < 0: + return None + return Stage(self.value - 1) + def to_pad(self) -> int: """ Maps stage value to a padding. The following mapping is used: @@ -465,11 +473,23 @@ def get_mask_invalid_actions_backward( ) return mask + def _update_state(self, stage: Stage): + """ + Updates the global state based on the states of the sub-environments and the + stage passed as an argument. + """ + return ( + [stage.value] + + self.composition.state + + self.space_group.state + + self.lattice_parameters.state + ) + def step( self, action: Tuple[int], skip_mask_check: bool = False ) -> Tuple[List[int], Tuple[int], bool]: """ - Executes forward step given an action. + Executes forward step given an action. The action is performed by the corresponding sub-environment and then the global state is updated accordingly. If the action is the EOS of the @@ -531,13 +551,63 @@ def step( else: raise ValueError(f"Unrecognized stage {stage}.") - if valid: - self.state = ( - [stage.value] - + self.composition.state - + self.space_group.state - + self.lattice_parameters.state - ) + self.state = self._update_state(stage) + return self.state, action, valid + + def step_backwards( + self, action: Tuple[int], skip_mask_check: bool = False + ) -> Tuple[List[int], Tuple[int], bool]: + """ + Executes backward step given an action. + + The action is performed by the corresponding sub-environment and then the + global state is updated accordingly. If the updated state of the + sub-environment becomes its source, the stage is decreased. + + Args + ---- + action : tuple + Action to be executed. The input action is global, that is padded. + + Returns + ------- + self.state : list + The state after executing the action. + + action : int + Action executed. + + valid : bool + False, if the action is not allowed for the current state. True otherwise. + """ + stage = self._get_stage(self.state) + # Skip mask check if stage is lattice parameters (continuous actions) + if stage == Stage.LATTICE_PARAMETERS: + skip_mask_check = True + # Replace action by its representative to check against the mask. + action_to_check = self.action2representative(action) + do_step, self.state, action_to_check = self._pre_step( + action_to_check, + backward=True, + skip_mask_check=(skip_mask_check or self.skip_mask_check), + ) + if not do_step: + return self.state, action, False + + # Call step of current subenvironment + action_subenv = self._depad_action(action, stage) + state_next, _, valid = self.subenvs[stage].step_backwards(action_subenv) + + # If action is invalid, exit immediately. Otherwise continue, + if not valid: + return self.state, action, False + self.n_actions += 1 + + # If next state is source of subenv, decrease stage. + if state_next == self.subenvs[stage].source: + stage = Stage.prev(stage) + + self.state = self._update_state(stage) return self.state, action, valid def _build_state(self, substate: List, stage: Stage) -> List: diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 3f2769b17..fa9e4bd4b 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -34,6 +34,13 @@ def test__stage_next__returns_expected(): assert Stage.next(Stage.DONE) == None +def test__stage_prev__returns_expected(): + assert Stage.prev(Stage.COMPOSITION) == None + assert Stage.prev(Stage.SPACE_GROUP) == Stage.COMPOSITION + assert Stage.prev(Stage.LATTICE_PARAMETERS) == Stage.SPACE_GROUP + assert Stage.prev(Stage.DONE) == Stage.LATTICE_PARAMETERS + + def test__environment__initializes_properly(env): pass From 75c49ffe623e1ca0700181268590d0cf383acd28 Mon Sep 17 00:00:00 2001 From: Alex Date: Sun, 24 Sep 2023 12:54:20 -0400 Subject: [PATCH 061/205] Return source in step_backwards if 0 stage is reached. --- gflownet/envs/crystals/ccrystal.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 0f6b6f9c8..d70d3e2b1 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -36,10 +36,10 @@ def next(self) -> "Stage": def prev(self) -> "Stage": """ - Returns the previous Stage in the enumeration or None if at the first stage. + Returns the previous Stage in the enumeration or DONE if from the first stage. """ if self.value - 1 < 0: - return None + return Stage.DONE return Stage(self.value - 1) def to_pad(self) -> int: @@ -606,6 +606,9 @@ def step_backwards( # If next state is source of subenv, decrease stage. if state_next == self.subenvs[stage].source: stage = Stage.prev(stage) + # If stage is DONE, return the global source + if stage is Stage.DONE: + return self.source, action, True self.state = self._update_state(stage) return self.state, action, valid From 499bdf07375983f960ebde828a0a41169938915f Mon Sep 17 00:00:00 2001 From: Alex Date: Sun, 24 Sep 2023 13:18:11 -0400 Subject: [PATCH 062/205] Minor docstrings fix --- gflownet/envs/cube.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 3f4e89291..a5a9df230 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -973,7 +973,7 @@ def get_logprobs( The output of the GFlowNet policy model. mask : tensor - The mask containing information invalid actions and special cases. + The mask containing information about invalid actions and special cases. actions : tensor The actions (absolute increments) from each state in the batch for which to From 934bec1abec9d8c0d4e2cebbb764f381e2428391 Mon Sep 17 00:00:00 2001 From: Alex Date: Sun, 24 Sep 2023 13:18:27 -0400 Subject: [PATCH 063/205] Implement get_logprobs --- gflownet/envs/crystals/ccrystal.py | 82 ++++++++++++++++++++++++++++++ 1 file changed, 82 insertions(+) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index d70d3e2b1..9cb670561 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -216,6 +216,26 @@ def _depad_action(self, action: Tuple[int], stage: Stage) -> Tuple[int]: return action[:dim] + # TODO: consider removing if unused because too simple + def _get_actions_of_subenv( + self, actions: TensorType["n_states", "action_dim"], stage: Stage + ): + """ + Returns the columns of a tensor of actions that correspond to the + sub-environment indicated by stage. + + Args + actions + mask : tensor + A tensor containing a batch of actions. It is assumed that all the rows in + the this tensor correspond to the same stage. + + stage : Stage + Identifier of the sub-environment of which the corresponding columns of the + actions are to be extracted. + """ + return actions[:, len(self.subenvs[stage].eos)] + def get_action_space(self) -> List[Tuple[int]]: composition_action_space = self._pad_action_space( self.composition.action_space, Stage.COMPOSITION @@ -697,6 +717,68 @@ def sample_actions_batch( ) return actions, None + def sample_actions_batch( + self, + policy_outputs: TensorType["n_states", "policy_output_dim"], + actions: TensorType["n_states", "actions_dim"], + mask: TensorType["n_states", "mask_dim"], + states_from: List, + is_backward: bool, + ) -> TensorType["batch_size"]: + """ + Computes log probabilities of actions given policy outputs and actions. + + Args + ---- + policy_outputs : tensor + The output of the GFlowNet policy model. + + mask : tensor + The mask containing information about invalid actions and special cases. + + actions : tensor + The actions (global) from each state in the batch for which to compute the + log probability. + + states_from : tensor + The states originating the actions, in GFlowNet format. + + is_backward : bool + True if the actions are backward, False if the actions are forward + (default). + """ + states_dict = {stage: [] for stage in Stage} + """ + A dictionary with keys equal to Stage and the values are the list of states in + the stage of the key. The states are only the part corresponding to the + sub-environment. + """ + stages = [] + for s in states_from: + stage = self._get_stage(s) + states_dict[stage].append(self._get_state_of_subenv(s, stage)) + stages.append(stage) + stages_tensor = tlong([stage.value for stage in stages], device=self.device) + is_subenv_dict = {stage: stages_tensor == stage.value for stage in Stage} + + # Compute logprobs from each sub-environment + logprobs = torch.empty( + policy_input_dim.shape[0], dtype=self.float, device=self.device + ) + for stage, subenv in self.subenvs.items(): + if not torch.any(is_subenv_dict[stage]): + continue + logprobs[is_subenv_dict[stage]] = subenv.get_logprobs( + self._get_policy_outputs_of_subenv( + policy_outputs[is_subenv_dict[stage]], stage + ), + actions[is_subenv_dict[stage], : len(subenv.eos)], + self._get_mask_of_subenv(mask[is_subenv_dict[stage]], stage), + states_dict[stage], + is_backward, + ) + return logprobs + # TODO: Consider removing altogether def get_parents( self, From a9e95b47b93ba9bd6e23cdda8a3715d11c4e419e Mon Sep 17 00:00:00 2001 From: Alex Date: Sun, 24 Sep 2023 13:38:50 -0400 Subject: [PATCH 064/205] Modify set_state. --- gflownet/envs/crystals/ccrystal.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 9cb670561..b0fdfdee3 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -885,17 +885,14 @@ def statetorch2oracle( def set_state(self, state: List, done: Optional[bool] = False): super().set_state(state, done) - stage = self._get_stage(state) - - composition_done = stage in [Stage.SPACE_GROUP, Stage.LATTICE_PARAMETERS] - space_group_done = stage == Stage.LATTICE_PARAMETERS - lattice_parameters_done = done - - self.composition.set_state(self._get_composition_state(state), composition_done) - self.space_group.set_state(self._get_space_group_state(state), space_group_done) - self.lattice_parameters.set_state( - self._get_lattice_parameters_state(state), lattice_parameters_done - ) + stage_idx = self._get_stage(state).value + + # Determine which subenvs are done based on stage and done + done_subenvs = [True] * stage_idx + [False] * (len(self.subenvs) - stage_idx) + done_subenvs[-1] = done + # Set state and done of each sub-environment + for (stage, subenv), subenv_done in zip(self.subenvs.items(), done_subenvs): + subenv.set_state(self._get_state_of_subenv(state, stage), subenv_done) """ We synchronize LatticeParameter's lattice system with the one of SpaceGroup From 861fe6296832617edb67de7584a8fb9403a50f1b Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Sun, 24 Sep 2023 18:32:17 -0400 Subject: [PATCH 065/205] remove missing elements padding --- gflownet/proxy/crystals/dave.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/gflownet/proxy/crystals/dave.py b/gflownet/proxy/crystals/dave.py index d261e6b90..2a111ffde 100644 --- a/gflownet/proxy/crystals/dave.py +++ b/gflownet/proxy/crystals/dave.py @@ -115,13 +115,6 @@ def __call__(self, states: TensorType["batch", "96"]) -> TensorType["batch"]: sg = states[:, -7] - 1 lat_params = states[:, -6:] - n_env = comp.shape[-1] - if n_env != self.model.n_elements: - missing = torch.zeros( - (len(comp), self.model.n_elements - n_env), device=comp.device - ) - comp = torch.cat([comp, missing], dim=-1) - if self.rescale_outputs: lat_params = (lat_params - self.scales["x"]["mean"]) / self.scales["x"][ "std" From 0715b186a4ce73a1b95ea4bc539dbb7799ffb8e5 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 18:32:57 -0400 Subject: [PATCH 066/205] Various fixes but something is broken --- gflownet/envs/crystals/ccrystal.py | 134 ++++++++++++++++++++++------- 1 file changed, 101 insertions(+), 33 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index b0fdfdee3..7f203553f 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -1,4 +1,5 @@ import json +from collections import OrderedDict from enum import Enum from typing import Dict, List, Optional, Tuple, Union @@ -89,11 +90,13 @@ def __init__( self.lattice_parameters = CLatticeParameters( lattice_system=TRICLINIC, **self.lattice_parameters_kwargs ) - self.subenvs = { - Stage.COMPOSITION: self.composition, - Stage.SPACE_GROUP: self.space_group, - Stage.LATTICE_PARAMETERS: self.lattice_parameters, - } + self.subenvs = OrderedDict( + { + Stage.COMPOSITION: self.composition, + Stage.SPACE_GROUP: self.space_group, + Stage.LATTICE_PARAMETERS: self.lattice_parameters, + } + ) # 0-th element of state encodes current stage: 0 for composition, # 1 for space group, 2 for lattice parameters @@ -316,7 +319,7 @@ def _get_policy_outputs_of_subenv( init_col = end_col def _get_mask_of_subenv( - self, mask: TensorType["n_states", "mask_dim"], stage: Stage + self, mask: Union[List, TensorType["n_states", "mask_dim"]], stage: Stage ): """ Returns the columns of a tensor of masks that correspond to the sub-environment @@ -324,9 +327,10 @@ def _get_mask_of_subenv( Args ---- - mask : tensor - A tensor containing a batch of masks. It is assumed that all the rows in - the this tensor correspond to the same stage. + mask : list or tensor + A mask of a single state as a list or a tensor containing a batch of masks. + It is assumed that all the rows in the this tensor correspond to the same + stage. stage : Stage Identifier of the sub-environment of which the corresponding columns of the @@ -336,7 +340,10 @@ def _get_mask_of_subenv( for stg, subenv in self.subenvs.items(): end_col = init_col + subenv.mask_dim if stg == stage: - return mask[:, init_col:end_col] + if isinstance(mask, list): + return mask[init_col:end_col] + else: + return mask[:, init_col:end_col] init_col = end_col def reset(self, env_id: Union[int, str] = None): @@ -449,6 +456,7 @@ def _get_lattice_parameters_tensor_states( :, self.lattice_parameters_state_start : self.lattice_parameters_state_end ] + # TODO: set mask of done state if stage is not the current one for correctness. def get_mask_invalid_actions_forward( self, state: Optional[List[int]] = None, done: Optional[bool] = None ) -> List[bool]: @@ -471,27 +479,64 @@ def get_mask_invalid_actions_forward( ) return mask + # TODO: this piece of code looks awful def get_mask_invalid_actions_backward( self, state: Optional[List[int]] = None, done: Optional[bool] = None ) -> List[bool]: """ Computes the backward actions mask of the state. - The mask of the parent crystal is simply the concatenation of the masks of the - three sub-environments. This assumes that the methods that will use the mask + The mask of the parent crystal is, in general, simply the concatenation of the + masks of the three sub-environments. Only the mask of the state of the current + sub-environment is computed; for the other sub-environments, the mask of the + source is used. Note that this assumes that the methods that will use the mask will extract the part corresponding to the relevant stage and ignore the rest. + + Nonetheless, in order to enable backward transitions between stages, the EOS + action of the preceding stage has to be the only valid action when the state of + a sub-environment is the source. Additionally, sample_batch_actions will have + to also detect the source states and change the stage. + + Note that the sub-environments are iterated in reversed order so as to save + unnecessary computations and simplify the code. """ state = self._get_state(state) done = self._get_done(done) + stage = self._get_stage(state) mask = [] - for stage, subenv in self.subenvs.items(): - mask.extend( - subenv.get_mask_invalid_actions_backward( - self._get_state_of_subenv(state, stage), done + do_eos_only = False + # Iterate stages in reverse order + for stg, subenv in reversed(self.subenvs.items()): + state_subenv = self._get_state_of_subenv(state, stg) + # Set mask of done state because state of next subenv is source + if do_eos_only: + mask_subenv = subenv.get_mask_invalid_actions_backward( + state_subenv, done=True ) - ) - return mask + do_eos_only = False + # General case + else: + # stg is the current stage + if stg == stage: + # state of subenv is the source state + if stg != Stage(0) and state_subenv == subenv.source: + do_eos_only = True + mask_subenv = subenv.get_mask_invalid_actions_backward( + subenv.source + ) + # General case + else: + mask_subenv = subenv.get_mask_invalid_actions_backward( + state_subenv, done + ) + # stg is not current stage, so set mask of source + else: + mask_subenv = subenv.get_mask_invalid_actions_backward( + subenv.source + ) + mask.extend(mask_subenv[::-1]) + return mask[::-1] def _update_state(self, stage: Stage): """ @@ -614,6 +659,14 @@ def step_backwards( if not do_step: return self.state, action, False + # If state of subenv is source of subenv, decrease stage + if self._get_state_of_subenv(self.state, stage) == self.subenvs[stage].source: + stage = Stage.prev(stage) + # If stage is DONE, set global source and return + if stage == Stage.DONE: + self.state = self.source + return self.state, action, True + # Call step of current subenvironment action_subenv = self._depad_action(action, stage) state_next, _, valid = self.subenvs[stage].step_backwards(action_subenv) @@ -623,13 +676,6 @@ def step_backwards( return self.state, action, False self.n_actions += 1 - # If next state is source of subenv, decrease stage. - if state_next == self.subenvs[stage].source: - stage = Stage.prev(stage) - # If stage is DONE, return the global source - if stage is Stage.DONE: - return self.source, action, True - self.state = self._update_state(stage) return self.state, action, valid @@ -687,7 +733,16 @@ def sample_actions_batch( stages = [] for s in states_from: stage = self._get_stage(s) - states_dict[stage].append(self._get_state_of_subenv(s, stage)) + state_subenv = self._get_state_of_subenv(s, stage) + # If the actions are backwards and state is source of subenv, decrease + # stage so that EOS of preceding stage is sampled. + if ( + is_backward + and stage != Stage(0) + and state_subenv == self.subenvs[stage].source + ): + stage = Stage.prev(stage) + states_dict[stage].append(state_subenv) stages.append(stage) stages_tensor = tlong([stage.value for stage in stages], device=self.device) is_subenv_dict = {stage: stages_tensor == stage.value for stage in Stage} @@ -717,7 +772,7 @@ def sample_actions_batch( ) return actions, None - def sample_actions_batch( + def get_logprobs( self, policy_outputs: TensorType["n_states", "policy_output_dim"], actions: TensorType["n_states", "actions_dim"], @@ -756,7 +811,16 @@ def sample_actions_batch( stages = [] for s in states_from: stage = self._get_stage(s) - states_dict[stage].append(self._get_state_of_subenv(s, stage)) + state_subenv = self._get_state_of_subenv(s, stage) + # If the actions are backwards and state is source of subenv, decrease + # stage so that EOS of preceding stage is sampled. + if ( + is_backward + and stage != Stage(0) + and state_subenv == self.subenvs[stage].source + ): + stage = Stage.prev(stage) + states_dict[stage].append(state_subenv) stages.append(stage) stages_tensor = tlong([stage.value for stage in stages], device=self.device) is_subenv_dict = {stage: stages_tensor == stage.value for stage in Stage} @@ -901,11 +965,15 @@ def set_state(self, state: List, done: Optional[bool] = False): and need to synchronize the LatticeParameter's lattice system to what that state indicates, """ - lattice_system = self.space_group.lattice_system - if lattice_system != "None": - self.lattice_parameters.lattice_system = lattice_system - else: - self.lattice_parameters.lattice_system = TRICLINIC + if self.space_group.done: + lattice_system = self.space_group.lattice_system + if lattice_system != "None": + self._set_lattice_parameters() + else: + self.lattice_parameters.lattice_system = TRICLINIC + # Set stoichiometry constraints in space group sub-environment + if self.do_stoichiometry_sg_check and self.composition.done: + self.space_group.set_n_atoms_compatibility_dict(self.composition.state) def state2readable(self, state: Optional[List[int]] = None) -> str: if state is None: From f4aacb33e2a6d57b9ab6496eca1804d5d70a1279 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 18:35:44 -0400 Subject: [PATCH 067/205] Minor changes in space group. --- gflownet/envs/crystals/spacegroup.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/gflownet/envs/crystals/spacegroup.py b/gflownet/envs/crystals/spacegroup.py index 8de313991..865e146c9 100644 --- a/gflownet/envs/crystals/spacegroup.py +++ b/gflownet/envs/crystals/spacegroup.py @@ -609,8 +609,6 @@ def set_n_atoms_compatibility_dict(self, n_atoms: List): removed from the list since they do not count towards the compatibility with a space group. """ - if n_atoms is not None: - n_atoms = [n for n in n_atoms if n > 0] # Get compatibility with stoichiometry self.n_atoms_compatibility_dict = SpaceGroup.build_n_atoms_compatibility_dict( n_atoms, self.space_groups.keys() @@ -642,8 +640,11 @@ def _is_compatible( return len(space_groups) > 0 + # TODO: this method is quite slow, consider improving efficiency. @staticmethod - def build_n_atoms_compatibility_dict(n_atoms: List[int], space_groups: List[int]): + def build_n_atoms_compatibility_dict( + n_atoms: List[int], space_groups: Iterable[int] + ): """ Obtains which space groups are compatible with the stoichiometry given as argument (n_atoms). It relies on pyxtal's @@ -655,8 +656,9 @@ def build_n_atoms_compatibility_dict(n_atoms: List[int], space_groups: List[int] Args ---- n_atoms : list of int - A list of positive number of atoms for each element in a stoichiometry. If - None, all space groups will be marked as compatible. + A list of number of atoms for each element in a stoichiometry. 0s will be + removed from the list since they do not count towards the compatibility + with a space group. If None, all space groups will be marked as compatible. space_groups : list of int A list of space group international numbers, in [1, 230] @@ -669,6 +671,7 @@ def build_n_atoms_compatibility_dict(n_atoms: List[int], space_groups: List[int] """ if n_atoms is None: return {sg: True for sg in space_groups} + n_atoms = [n for n in n_atoms if n > 0] assert all([n > 0 for n in n_atoms]) assert all([sg > 0 and sg <= 230 for sg in space_groups]) return {sg: Group(sg).check_compatible(n_atoms)[0] for sg in space_groups} From 2380e5f256e7debb4de4324649c2aed37594e9db Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 18:35:54 -0400 Subject: [PATCH 068/205] Various changes in tests --- tests/gflownet/envs/test_ccrystal.py | 280 ++++++++++++++++++++++++++- 1 file changed, 279 insertions(+), 1 deletion(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index fa9e4bd4b..b913a5ed8 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -35,7 +35,7 @@ def test__stage_next__returns_expected(): def test__stage_prev__returns_expected(): - assert Stage.prev(Stage.COMPOSITION) == None + assert Stage.prev(Stage.COMPOSITION) == Stage.DONE assert Stage.prev(Stage.SPACE_GROUP) == Stage.COMPOSITION assert Stage.prev(Stage.LATTICE_PARAMETERS) == Stage.SPACE_GROUP assert Stage.prev(Stage.DONE) == Stage.LATTICE_PARAMETERS @@ -79,6 +79,145 @@ def test__pad_depad_action(env): assert depadded == action +@pytest.mark.parametrize( + "env_input, state, dones, has_lattice_parameters, has_composition_constraints", + [ + ( + "env", + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [False, False, False], + False, + False, + ), + ( + "env", + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [False, False, False], + False, + False, + ), + ( + "env", + [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + [True, False, False], + True, + False, + ), + ( + "env", + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [True, True, False], + True, + False, + ), + ( + "env_with_stoichiometry_sg_check", + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [True, True, False], + True, + True, + ), + ], +) +def test__set_state__sets_state_subenvs_dones_and_constraints( + env_input, + state, + dones, + has_lattice_parameters, + has_composition_constraints, + request, +): + env = request.getfixturevalue(env_input) + env.set_state(state) + # Check global state + assert env.state == state + + # Check states of subenvs + for stage, subenv in env.subenvs.items(): + assert subenv.state == env._get_state_of_subenv(state, stage) + + # Check dones + for subenv, done in zip(env.subenvs.values(), dones): + assert subenv.done == done + + # Check lattice parameters + if env.space_group.lattice_system != "None": + assert has_lattice_parameters + assert env.space_group.lattice_system == env.lattice_parameters.lattice_system + else: + assert not has_lattice_parameters + + # Check composition constraints + if has_composition_constraints: + n_atoms_compatibility_dict = env.space_group.build_n_atoms_compatibility_dict( + env.composition.state, env.space_group.space_groups.keys() + ) + assert n_atoms_compatibility_dict == env.space_group.n_atoms_compatibility_dict + + +@pytest.mark.parametrize( + "state", + [ + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 2, 2, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.12, 0.23, 0.34, 0.45, 0.56, 0.67], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + ], +) +def test__get_mask_invald_actions_backward__returns_expected_general_case(env, state): + stage = env._get_stage(state) + mask = env.get_mask_invalid_actions_backward(state, done=False) + for stg, subenv in env.subenvs.items(): + if stg == stage: + # Mask of state if stage is current stage in state + mask_subenv_expected = subenv.get_mask_invalid_actions_backward( + env._get_state_of_subenv(state, stg) + ) + else: + # Mask of source if stage is other than current stage in state + mask_subenv_expected = subenv.get_mask_invalid_actions_backward( + subenv.source + ) + mask_subenv = env._get_mask_of_subenv(mask, stg) + assert mask_subenv == mask_subenv_expected + + +@pytest.mark.parametrize( + "state", + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 3, 1, 0, 6, 1, 2, 2, -1, -1, -1, -1, -1, -1], + [2, 3, 1, 0, 6, 2, 1, 3, -1, -1, -1, -1, -1, -1], + ], +) +def test__get_mask_invald_actions_backward__returns_expected_stage_transition(env, state): + stage = env._get_stage(state) + mask = env.get_mask_invalid_actions_backward(state, done=False) + for stg, subenv in env.subenvs.items(): + if stg == Stage.prev(stage) and stage != Stage(0): + # Mask of done (EOS only) if stage is previous stage in state + mask_subenv_expected = subenv.get_mask_invalid_actions_backward( + env._get_state_of_subenv(state, stg), done=True + ) + else: + mask_subenv_expected = subenv.get_mask_invalid_actions_backward( + subenv.source + ) + if stg == stage: + assert env._get_state_of_subenv(state, stg) == subenv.source + mask_subenv = env._get_mask_of_subenv(mask, stg) + assert mask_subenv == mask_subenv_expected + + @pytest.mark.skip(reason="skip until updated") @pytest.mark.parametrize( "state, expected", @@ -289,6 +428,144 @@ def test__step__action_sequence_has_expected_result( assert valid == last_action_valid +@pytest.mark.skip(reason="skip until updated") +@pytest.mark.parametrize( + "state_init, state_end, stage_init, stage_end, actions, last_action_valid", + [ + [ + [0, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.COMPOSITION, + Stage.COMPOSITION, + [(3, 4, -2, -2, -2, -2, -2), (1, 1, -2, -2, -2, -2, -2)], + True, + ], + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.COMPOSITION, + Stage.COMPOSITION, + [(2, 105, 3, -3, -3, -3, -3)], + False, + ], + [ + [1, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.SPACE_GROUP, + Stage.COMPOSITION, + [ + (-1, -1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (1, 1, -2, -2, -2, -2, -2), + ], + True, + ], + [ + [1, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.SPACE_GROUP, + Stage.COMPOSITION, + [ + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (1, 1, -2, -2, -2, -2, -2), + ], + True, + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.LATTICE_PARAMETERS, + Stage.COMPOSITION, + [ + (-1, -1, -1, -3, -3, -3, -3), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (1, 1, -2, -2, -2, -2, -2), + ], + True, + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + Stage.LATTICE_PARAMETERS, + Stage.LATTICE_PARAMETERS, + [ + (1.5, 0, 0, 0, 0, 0, 0), + ], + False, + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.LATTICE_PARAMETERS, + Stage.COMPOSITION, + [ + (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (-1, -1, -1, -3, -3, -3, -3), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (1, 1, -2, -2, -2, -2, -2), + ], + True, + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.LATTICE_PARAMETERS, + Stage.COMPOSITION, + [ + (0.66, 0.55, 0.44, 0.33, 0.22, 0.11, 0), + (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (-1, -1, -1, -3, -3, -3, -3), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (1, 1, -2, -2, -2, -2, -2), + ], + True, + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.LATTICE_PARAMETERS, + Stage.COMPOSITION, + [ + (np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf), + (0.66, 0.55, 0.44, 0.33, 0.22, 0.11, 0), + (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (-1, -1, -1, -3, -3, -3, -3), + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (1, 1, -2, -2, -2, -2, -2), + ], + True, + ], + ], +) +def test__step_backwards__action_sequence_has_expected_result( + env, state_init, state_end, stage_init, stage_end, actions, last_action_valid +): + # Hacky way to also test if first action global EOS + if actions[0] == env.eos: + env.set_state(state_init, done=True) + else: + env.set_state(state_init, done=False) + assert env.state == state_init + assert env._get_stage() == stage_init + for action in actions: + warnings.filterwarnings("ignore") + _, _, valid = env.step_backwards(action) + + assert env.state == state_end + assert env._get_stage() == stage_end + assert valid == last_action_valid + + # TODO: Remove if get_parents is removed @pytest.mark.parametrize( "actions", @@ -342,6 +619,7 @@ def test__reset(env, actions): assert env.lattice_parameters.lattice_system == TRICLINIC +# TODO: write new test of masks, both fw and bw @pytest.mark.skip(reason="skip while developping other tests") @pytest.mark.parametrize( "actions, exp_stage", From 411a359962422453371c7ae68c6368af832c2e62 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 20:52:18 -0400 Subject: [PATCH 069/205] Implement set_lattice_system() --- gflownet/envs/crystals/clattice_parameters.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index a1882689a..82345b761 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -132,6 +132,13 @@ def _get_index_of_param(self, param): else: return None + def set_lattice_system(self, lattice_system: str): + """ + Sets the lattice system of the unit cell and updates the constraints. + """ + self.lattice_system = lattice_system + self._setup_constraints() + def _setup_constraints(self): """ Computes the mask of ignored dimensions, given the constraints imposed by the From 35f803972ddec11c91564d9a74ceee02885b4e4d Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 20:53:12 -0400 Subject: [PATCH 070/205] Big replacement of previous code by new code based on dict of subenvs. --- gflownet/envs/crystals/ccrystal.py | 380 ++++++------------ tests/gflownet/envs/test_ccrystal.py | 576 +++++++++++++++++++-------- 2 files changed, 516 insertions(+), 440 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 7f203553f..f2e4bc7cc 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -82,77 +82,37 @@ def __init__( self.lattice_parameters_kwargs = lattice_parameters_kwargs or {} self.do_stoichiometry_sg_check = do_stoichiometry_sg_check - self.composition = Composition(**self.composition_kwargs) - self.space_group = SpaceGroup(**self.space_group_kwargs) + composition = Composition(**self.composition_kwargs) + space_group = SpaceGroup(**self.space_group_kwargs) # We initialize lattice parameters with triclinic lattice system as it is the # most general one, but it will have to be reinitialized using proper lattice # system from space group once that is determined. - self.lattice_parameters = CLatticeParameters( + lattice_parameters = CLatticeParameters( lattice_system=TRICLINIC, **self.lattice_parameters_kwargs ) self.subenvs = OrderedDict( { - Stage.COMPOSITION: self.composition, - Stage.SPACE_GROUP: self.space_group, - Stage.LATTICE_PARAMETERS: self.lattice_parameters, + Stage.COMPOSITION: composition, + Stage.SPACE_GROUP: space_group, + Stage.LATTICE_PARAMETERS: lattice_parameters, } ) # 0-th element of state encodes current stage: 0 for composition, # 1 for space group, 2 for lattice parameters - self.source = ( - [Stage.COMPOSITION.value] - + self.composition.source - + self.space_group.source - + self.lattice_parameters.source - ) - - # start and end indices of individual substates - self.composition_state_start = 1 - self.composition_state_end = self.composition_state_start + len( - self.composition.source - ) - self.space_group_state_start = self.composition_state_end - self.space_group_state_end = self.space_group_state_start + len( - self.space_group.source - ) - self.lattice_parameters_state_start = self.space_group_state_end - self.lattice_parameters_state_end = self.lattice_parameters_state_start + len( - self.lattice_parameters.source - ) + self.source = [Stage.COMPOSITION.value] + for subenv in self.subenvs.values(): + self.source.extend(subenv.source) - # start and end indices of individual submasks - self.composition_mask_start = 0 - self.composition_mask_end = self.composition_mask_start + len( - self.composition.action_space - ) - self.space_group_mask_start = self.composition_mask_end - self.space_group_mask_end = self.space_group_mask_start + len( - self.space_group.action_space - ) - self.lattice_parameters_mask_start = self.space_group_mask_end - self.lattice_parameters_mask_end = self.lattice_parameters_mask_start + len( - self.lattice_parameters.action_space - ) - - self.composition_action_length = max( - len(a) for a in self.composition.action_space - ) - self.space_group_action_length = max( - len(a) for a in self.space_group.action_space - ) - self.lattice_parameters_action_length = max( - len(a) for a in self.lattice_parameters.action_space - ) + # Get action dimensionality by computing the maximum action length among all + # sub-environments. self.max_action_length = max( - self.composition_action_length, - self.space_group_action_length, - self.lattice_parameters_action_length, + [len(subenv.eos) for subenv in self.subenvs.values()] ) - # EOS is EOS of LatticeParameters because it is the last stage + # EOS is EOS of the last stage (lattice parameters) self.eos = self._pad_action( - self.lattice_parameters.eos, Stage.LATTICE_PARAMETERS + self.subenvs[Stage.LATTICE_PARAMETERS].eos, Stage.LATTICE_PARAMETERS ) # Mask dimensionality @@ -167,28 +127,31 @@ def __init__( # Since only the lattice parameters subenv has distribution parameters, only # these are pased to the base init. super().__init__( - fixed_distr_params=self.lattice_parameters.fixed_distr_params, - random_distr_params=self.lattice_parameters.random_distr_params, + fixed_distr_params=self.subenvs[ + Stage.LATTICE_PARAMETERS + ].fixed_distr_params, + random_distr_params=self.subenvs[ + Stage.LATTICE_PARAMETERS + ].random_distr_params, **kwargs, ) self.continuous = True + # TODO: remove or redo def _set_lattice_parameters(self): """ Sets CLatticeParameters conditioned on the lattice system derived from the SpaceGroup. """ - if self.space_group.lattice_system == "None": + if self.subenvs[Stage.SPACE_GROUP].lattice_system == "None": raise ValueError( "Cannot set lattice parameters without lattice system determined in " "the space group." ) - - self.lattice_parameters = CLatticeParameters( - lattice_system=self.space_group.lattice_system, + self.subenvs[Stage.LATTICE_PARAMETERS] = CLatticeParameters( + lattice_system=self.subenvs[Stage.SPACE_GROUP].lattice_system, **self.lattice_parameters_kwargs, ) - self.subenvs[Stage.LATTICE_PARAMETERS] = self.lattice_parameters def _pad_action(self, action: Tuple[int], stage: Stage) -> Tuple[int]: """ @@ -208,16 +171,7 @@ def _depad_action(self, action: Tuple[int], stage: Stage) -> Tuple[int]: Reverses padding operation, such that the resulting action can be passed to the underlying environment. """ - if stage == Stage.COMPOSITION: - dim = self.composition_action_length - elif stage == Stage.SPACE_GROUP: - dim = self.space_group_action_length - elif stage == Stage.LATTICE_PARAMETERS: - dim = self.lattice_parameters_action_length - else: - raise ValueError(f"Unrecognized stage {stage}.") - - return action[:dim] + return action[: len(self.subenvs[stage].eos)] # TODO: consider removing if unused because too simple def _get_actions_of_subenv( @@ -240,21 +194,9 @@ def _get_actions_of_subenv( return actions[:, len(self.subenvs[stage].eos)] def get_action_space(self) -> List[Tuple[int]]: - composition_action_space = self._pad_action_space( - self.composition.action_space, Stage.COMPOSITION - ) - space_group_action_space = self._pad_action_space( - self.space_group.action_space, Stage.SPACE_GROUP - ) - lattice_parameters_action_space = self._pad_action_space( - self.lattice_parameters.action_space, Stage.LATTICE_PARAMETERS - ) - - action_space = ( - composition_action_space - + space_group_action_space - + lattice_parameters_action_space - ) + action_space = [] + for stage, subenv in self.subenvs.items(): + action_space.extend(self._pad_action_space(subenv.action_space, stage)) if len(action_space) != len(set(action_space)): raise ValueError( @@ -271,17 +213,13 @@ def action2representative(self, action: Tuple) -> Tuple: action space. """ if self._get_stage() == Stage.LATTICE_PARAMETERS: - return self.lattice_parameters.action2representative( + return self.subenvs[Stage.LATTICE_PARAMETERS].action2representative( self._depad_action(action, Stage.LATTICE_PARAMETERS) ) return action def get_max_traj_length(self) -> int: - return ( - self.composition.get_max_traj_length() - + self.space_group.get_max_traj_length() - + self.lattice_parameters.get_max_traj_length() - ) + return sum([subenv.get_max_traj_length() for subenv in self.subenvs.values()]) def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: """ @@ -347,9 +285,9 @@ def _get_mask_of_subenv( init_col = end_col def reset(self, env_id: Union[int, str] = None): - self.composition.reset() - self.space_group.reset() - self.lattice_parameters = CLatticeParameters( + self.subenvs[Stage.COMPOSITION].reset() + self.subenvs[Stage.SPACE_GROUP].reset() + self.subenvs[Stage.LATTICE_PARAMETERS] = CLatticeParameters( lattice_system=TRICLINIC, **self.lattice_parameters_kwargs ) @@ -420,41 +358,28 @@ def _get_state_of_subenv(self, state: List, stage: Optional[Stage] = None): return state[init_col:end_col] init_col = end_col - def _get_composition_state(self, state: Optional[List[int]] = None) -> List[int]: - state = self._get_state(state) - - return state[self.composition_state_start : self.composition_state_end] - - def _get_composition_tensor_states( - self, states: TensorType["batch", "state_dim"] - ) -> TensorType["batch", "state_oracle_dim"]: - return states[:, self.composition_state_start : self.composition_state_end] - - def _get_space_group_state(self, state: Optional[List[int]] = None) -> List[int]: - state = self._get_state(state) - - return state[self.space_group_state_start : self.space_group_state_end] - - def _get_space_group_tensor_states( - self, states: TensorType["batch", "state_dim"] - ) -> TensorType["batch", "state_oracle_dim"]: - return states[:, self.space_group_state_start : self.space_group_state_end] - - def _get_lattice_parameters_state( - self, state: Optional[List[int]] = None - ) -> List[int]: - state = self._get_state(state) + def _get_states_of_subenv( + self, states: TensorType["n_states", "state_dim"], stage: Stage + ): + """ + Returns the part of the batch of states corresponding to the subenv indicated + by stage. - return state[ - self.lattice_parameters_state_start : self.lattice_parameters_state_end - ] + Args + ---- + states : tensor + A batch of states of the parent Crystal environment. - def _get_lattice_parameters_tensor_states( - self, states: TensorType["batch", "state_dim"] - ) -> TensorType["batch", "state_oracle_dim"]: - return states[ - :, self.lattice_parameters_state_start : self.lattice_parameters_state_end - ] + stage : Stage + Identifier of the sub-environment of which the corresponding part of the + states is to be extracted. If None, it is inferred from the states. + """ + init_col = 1 + for stg, subenv in self.subenvs.items(): + end_col = init_col + len(subenv.source) + if stg == stage: + return states[:, init_col:end_col] + init_col = end_col # TODO: set mask of done state if stage is not the current one for correctness. def get_mask_invalid_actions_forward( @@ -543,12 +468,10 @@ def _update_state(self, stage: Stage): Updates the global state based on the states of the sub-environments and the stage passed as an argument. """ - return ( - [stage.value] - + self.composition.state - + self.space_group.state - + self.lattice_parameters.state - ) + state = [stage.value] + for subenv in self.subenvs.values(): + state.extend(subenv.state) + return state def step( self, action: Tuple[int], skip_mask_check: bool = False @@ -604,11 +527,14 @@ def step( stage = Stage.next(stage) if stage == Stage.SPACE_GROUP: if self.do_stoichiometry_sg_check: - self.space_group.set_n_atoms_compatibility_dict( - self.composition.state + self.subenvs[Stage.SPACE_GROUP].set_n_atoms_compatibility_dict( + self.subenvs[Stage.COMPOSITION].state ) elif stage == Stage.LATTICE_PARAMETERS: - self._set_lattice_parameters() + lattice_system = self.subenvs[Stage.SPACE_GROUP].lattice_system + self.subenvs[Stage.LATTICE_PARAMETERS].set_lattice_system( + lattice_system + ) elif stage == Stage.DONE: self.n_actions += 1 self.done = True @@ -676,32 +602,14 @@ def step_backwards( return self.state, action, False self.n_actions += 1 + # If action from done, set done False + if self.done: + assert action == self.eos + self.done = False + self.state = self._update_state(stage) return self.state, action, valid - def _build_state(self, substate: List, stage: Stage) -> List: - """ - Converts the state coming from one of the subenvironments into a combined state - format used by the Crystal environment. - """ - if stage == Stage.COMPOSITION: - output = ( - [0] - + substate - + self.space_group.source - + self.lattice_parameters.source - ) - elif stage == Stage.SPACE_GROUP: - output = ( - [1] + self.composition.state + substate + self.lattice_parameters.source - ) - elif stage == Stage.LATTICE_PARAMETERS: - output = [2] + self.composition.state + self.space_group.state + substate - else: - raise ValueError(f"Unrecognized stage {stage}.") - - return output - def sample_actions_batch( self, policy_outputs: TensorType["n_states", "policy_output_dim"], @@ -802,6 +710,7 @@ def get_logprobs( True if the actions are backward, False if the actions are forward (default). """ + n_states = policy_outputs.shape[0] states_dict = {stage: [] for stage in Stage} """ A dictionary with keys equal to Stage and the values are the list of states in @@ -826,9 +735,7 @@ def get_logprobs( is_subenv_dict = {stage: stages_tensor == stage.value for stage in Stage} # Compute logprobs from each sub-environment - logprobs = torch.empty( - policy_input_dim.shape[0], dtype=self.float, device=self.device - ) + logprobs = torch.empty(n_states, dtype=self.float, device=self.device) for stage, subenv in self.subenvs.items(): if not torch.any(is_subenv_dict[stage]): continue @@ -843,79 +750,19 @@ def get_logprobs( ) return logprobs - # TODO: Consider removing altogether - def get_parents( - self, - state: Optional[List] = None, - done: Optional[bool] = None, - action: Optional[Tuple] = None, - ) -> Tuple[List, List]: - state = self._get_state(state) - done = self._get_done(done) - stage = self._get_stage(state) - - if done: - return [state], [self.eos] - - if stage == Stage.COMPOSITION or ( - stage == Stage.SPACE_GROUP - and self._get_space_group_state(state) == self.space_group.source - ): - composition_done = stage == Stage.SPACE_GROUP - parents, actions = self.composition.get_parents( - state=self._get_composition_state(state), done=composition_done - ) - parents = [self._build_state(p, Stage.COMPOSITION) for p in parents] - actions = [self._pad_action(a, Stage.COMPOSITION) for a in actions] - elif stage == Stage.SPACE_GROUP or ( - stage == Stage.LATTICE_PARAMETERS - and self._get_lattice_parameters_state(state) - == self.lattice_parameters.source - ): - space_group_done = stage == Stage.LATTICE_PARAMETERS - parents, actions = self.space_group.get_parents( - state=self._get_space_group_state(state), done=space_group_done - ) - parents = [self._build_state(p, Stage.SPACE_GROUP) for p in parents] - actions = [self._pad_action(a, Stage.SPACE_GROUP) for a in actions] - elif stage == Stage.LATTICE_PARAMETERS: - """ - get_parents() is not well defined for continuous environment. Here we - simply return the same state and the representative action. - """ - parents = [state] - actions = [self.action2representative(action)] - else: - raise ValueError(f"Unrecognized stage {stage}.") - - return parents, actions - def state2oracle(self, state: Optional[List[int]] = None) -> Tensor: """ Prepares a list of states in "GFlowNet format" for the oracle. Simply a concatenation of all crystal components. """ - if state is None: - state = self.state.copy() - - composition_oracle_state = self.composition.state2oracle( - state=self._get_composition_state(state) - ).to(self.device) - space_group_oracle_state = ( - self.space_group.state2oracle(state=self._get_space_group_state(state)) - .unsqueeze(-1) # StateGroup oracle state is a single number - .to(self.device) - ) - lattice_parameters_oracle_state = self.lattice_parameters.state2oracle( - state=self._get_lattice_parameters_state(state) - ).to(self.device) + state = self._get_state(state) + # TODO: Might break because StateGroup oracle state is a single number return torch.cat( - [ - composition_oracle_state, - space_group_oracle_state, - lattice_parameters_oracle_state, - ] + ( + subenv.state2oracle(self._get_state_of_subenv(state, stage)) + for stage, subenv in self.subenvs + ) ) def statebatch2oracle( @@ -928,21 +775,11 @@ def statebatch2oracle( def statetorch2oracle( self, states: TensorType["batch", "state_dim"] ) -> TensorType["batch", "state_oracle_dim"]: - composition_oracle_states = self.composition.statetorch2oracle( - self._get_composition_tensor_states(states) - ).to(self.device) - space_group_oracle_states = self.space_group.statetorch2oracle( - self._get_space_group_tensor_states(states) - ).to(self.device) - lattice_parameters_oracle_states = self.lattice_parameters.statetorch2oracle( - self._get_lattice_parameters_tensor_states(states) - ).to(self.device) return torch.cat( - [ - composition_oracle_states, - space_group_oracle_states, - lattice_parameters_oracle_states, - ], + ( + subenv.statetorch2oracle(self._get_states_of_subenv(states, stage)) + for stage, subenv in self.subenvs + ), dim=1, ) @@ -965,29 +802,31 @@ def set_state(self, state: List, done: Optional[bool] = False): and need to synchronize the LatticeParameter's lattice system to what that state indicates, """ - if self.space_group.done: - lattice_system = self.space_group.lattice_system + if self.subenvs[Stage.SPACE_GROUP].done: + lattice_system = self.subenvs[Stage.SPACE_GROUP].lattice_system if lattice_system != "None": - self._set_lattice_parameters() + self.subenvs[Stage.LATTICE_PARAMETERS].set_lattice_system( + lattice_system + ) else: - self.lattice_parameters.lattice_system = TRICLINIC + self.subenvs[Stage.LATTICE_PARAMETERS].set_lattice_system(TRICLINIC) # Set stoichiometry constraints in space group sub-environment - if self.do_stoichiometry_sg_check and self.composition.done: - self.space_group.set_n_atoms_compatibility_dict(self.composition.state) + if self.do_stoichiometry_sg_check and self.subenvs[Stage.COMPOSITION].done: + self.subenvs[Stage.SPACE_GROUP].set_n_atoms_compatibility_dict( + self.subenvs[Stage.COMPOSITION].state + ) def state2readable(self, state: Optional[List[int]] = None) -> str: if state is None: state = self.state - composition_readable = self.composition.state2readable( - state=self._get_composition_state(state) - ) - space_group_readable = self.space_group.state2readable( - state=self._get_space_group_state(state) - ) - lattice_parameters_readable = self.lattice_parameters.state2readable( - state=self._get_lattice_parameters_state(state) - ) + readables = [ + subenv.state2readable(self._get_state_of_subenv(state, stage)) + for stage, subenv in self.subenvs + ] + composition_readable = readables[0] + space_group_readable = readables[1] + lattice_parameters_readable = readables[2] return ( f"Stage = {state[0]}; " @@ -996,15 +835,18 @@ def state2readable(self, state: Optional[List[int]] = None) -> str: f"LatticeParameters = {lattice_parameters_readable}" ) - def readable2state(self, readable: str) -> List[int]: - splits = readable.split("; ") - readables = [x.split(" = ")[1] for x in splits] + # TODO: redo - return ( - [int(readables[0])] - + self.composition.readable2state( - json.loads(readables[1].replace("'", '"')) - ) - + self.space_group.readable2state(readables[2]) - + self.lattice_parameters.readable2state(readables[3]) - ) + +# def readable2state(self, readable: str) -> List[int]: +# splits = readable.split("; ") +# readables = [x.split(" = ")[1] for x in splits] +# +# return ( +# [int(readables[0])] +# + self.composition.readable2state( +# json.loads(readables[1].replace("'", '"')) +# ) +# + self.space_group.readable2state(readables[2]) +# + self.lattice_parameters.readable2state(readables[3]) +# ) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index b913a5ed8..c0d411a95 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -56,14 +56,16 @@ def test__environment__has_expected_initial_state(env): def test__environment__has_expected_action_space(env): - assert len(env.action_space) == len(env.composition.action_space) + len( - env.space_group.action_space - ) + len(env.lattice_parameters.action_space) + assert len(env.action_space) == len( + env.subenvs[Stage.COMPOSITION].action_space + ) + len(env.subenvs[Stage.SPACE_GROUP].action_space) + len( + env.subenvs[Stage.LATTICE_PARAMETERS].action_space + ) underlying_action_space = ( - env.composition.action_space - + env.space_group.action_space - + env.lattice_parameters.action_space + env.subenvs[Stage.COMPOSITION].action_space + + env.subenvs[Stage.SPACE_GROUP].action_space + + env.subenvs[Stage.LATTICE_PARAMETERS].action_space ) for action, underlying_action in zip(env.action_space, underlying_action_space): @@ -79,6 +81,92 @@ def test__pad_depad_action(env): assert depadded == action +@pytest.mark.parametrize( + "state, state_composition, state_space_group, state_lattice_parameters", + [ + [ + [0, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [0, 0, 0], + [-1, -1, -1, -1, -1, -1], + ], + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 0, 0], + [0, 0, 0], + [-1, -1, -1, -1, -1, -1], + ], + [ + [1, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [0, 0, 0], + [-1, -1, -1, -1, -1, -1], + ], + [ + [1, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [4, 3, 105], + [-1, -1, -1, -1, -1, -1], + ], + [ + [1, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [4, 3, 105], + [-1, -1, -1, -1, -1, -1], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [4, 3, 105], + [-1, -1, -1, -1, -1, -1], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 4, 0], + [4, 3, 105], + [-1, -1, -1, -1, -1, -1], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [1, 0, 4, 0], + [4, 3, 105], + [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [1, 0, 4, 0], + [4, 3, 105], + [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [1, 0, 4, 0], + [4, 3, 105], + [0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + ], + [ + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [1, 0, 4, 0], + [4, 3, 105], + [0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + ], + ], +) +def test__state_of_subenv__returns_expected( + env, state, state_composition, state_space_group, state_lattice_parameters +): + for stage in env.subenvs: + state_subenv = env._get_state_of_subenv(state, stage) + if stage == Stage.COMPOSITION: + assert state_subenv == state_composition + elif stage == Stage.SPACE_GROUP: + assert state_subenv == state_space_group + elif stage == Stage.LATTICE_PARAMETERS: + assert state_subenv == state_lattice_parameters + else: + raise ValueError(f"Unrecognized stage {stage}.") + + @pytest.mark.parametrize( "env_input, state, dones, has_lattice_parameters, has_composition_constraints", [ @@ -141,18 +229,27 @@ def test__set_state__sets_state_subenvs_dones_and_constraints( assert subenv.done == done # Check lattice parameters - if env.space_group.lattice_system != "None": + if env.subenvs[Stage.SPACE_GROUP].lattice_system != "None": assert has_lattice_parameters - assert env.space_group.lattice_system == env.lattice_parameters.lattice_system + assert ( + env.subenvs[Stage.SPACE_GROUP].lattice_system + == env.subenvs[Stage.LATTICE_PARAMETERS].lattice_system + ) else: assert not has_lattice_parameters # Check composition constraints if has_composition_constraints: - n_atoms_compatibility_dict = env.space_group.build_n_atoms_compatibility_dict( - env.composition.state, env.space_group.space_groups.keys() + n_atoms_compatibility_dict = env.subenvs[ + Stage.SPACE_GROUP + ].build_n_atoms_compatibility_dict( + env.subenvs[Stage.COMPOSITION].state, + env.subenvs[Stage.SPACE_GROUP].space_groups.keys(), + ) + assert ( + n_atoms_compatibility_dict + == env.subenvs[Stage.SPACE_GROUP].n_atoms_compatibility_dict ) - assert n_atoms_compatibility_dict == env.space_group.n_atoms_compatibility_dict @pytest.mark.parametrize( @@ -171,7 +268,7 @@ def test__set_state__sets_state_subenvs_dones_and_constraints( [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], ], ) -def test__get_mask_invald_actions_backward__returns_expected_general_case(env, state): +def test__get_mask_invalid_actions_backward__returns_expected_general_case(env, state): stage = env._get_stage(state) mask = env.get_mask_invalid_actions_backward(state, done=False) for stg, subenv in env.subenvs.items(): @@ -199,7 +296,9 @@ def test__get_mask_invald_actions_backward__returns_expected_general_case(env, s [2, 3, 1, 0, 6, 2, 1, 3, -1, -1, -1, -1, -1, -1], ], ) -def test__get_mask_invald_actions_backward__returns_expected_stage_transition(env, state): +def test__get_mask_invald_actions_backward__returns_expected_stage_transition( + env, state +): stage = env._get_stage(state) mask = env.get_mask_invalid_actions_backward(state, done=False) for stg, subenv in env.subenvs.items(): @@ -367,7 +466,7 @@ def test__step__single_action_works(env, action): (-1, -1, -1, -3, -3, -3, -3), (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), ], - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], Stage.LATTICE_PARAMETERS, True, ], @@ -379,9 +478,9 @@ def test__step__single_action_works(env, action): (2, 105, 0, -3, -3, -3, -3), (-1, -1, -1, -3, -3, -3, -3), (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), - (0.6, 0.5, 0.4, 0.3, 0.2, 0.6, 0), + (0.6, 0.5, 0.8, 0.3, 0.2, 0.6, 0), ], - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], Stage.LATTICE_PARAMETERS, False, ], @@ -392,10 +491,10 @@ def test__step__single_action_works(env, action): (-1, -1, -2, -2, -2, -2, -2), (2, 105, 0, -3, -3, -3, -3), (-1, -1, -1, -3, -3, -3, -3), - (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), - (0.66, 0.55, 0.44, 0.33, 0.22, 0.11, 0), + (0.1, 0.1, 0.3, 0.0, 0.0, 0.0, 1), + (0.66, 0.0, 0.44, 0.0, 0.0, 0.0, 0), ], - [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], Stage.LATTICE_PARAMETERS, True, ], @@ -406,11 +505,11 @@ def test__step__single_action_works(env, action): (-1, -1, -2, -2, -2, -2, -2), (2, 105, 0, -3, -3, -3, -3), (-1, -1, -1, -3, -3, -3, -3), - (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), - (0.66, 0.55, 0.44, 0.33, 0.22, 0.11, 0), + (0.1, 0.1, 0.3, 0.0, 0.0, 0.0, 1), + (0.66, 0.66, 0.44, 0.0, 0.0, 0.0, 0), (np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf), ], - [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], Stage.LATTICE_PARAMETERS, True, ], @@ -428,7 +527,6 @@ def test__step__action_sequence_has_expected_result( assert valid == last_action_valid -@pytest.mark.skip(reason="skip until updated") @pytest.mark.parametrize( "state_init, state_end, stage_init, stage_end, actions, last_action_valid", [ @@ -488,8 +586,8 @@ def test__step__action_sequence_has_expected_result( True, ], [ - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], Stage.LATTICE_PARAMETERS, Stage.LATTICE_PARAMETERS, [ @@ -498,12 +596,12 @@ def test__step__action_sequence_has_expected_result( False, ], [ - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.LATTICE_PARAMETERS, Stage.COMPOSITION, [ - (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (0.1, 0.1, 0.3, 0.0, 0.0, 0.0, 1), (-1, -1, -1, -3, -3, -3, -3), (2, 105, 0, -3, -3, -3, -3), (-1, -1, -2, -2, -2, -2, -2), @@ -513,13 +611,13 @@ def test__step__action_sequence_has_expected_result( True, ], [ - [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.LATTICE_PARAMETERS, Stage.COMPOSITION, [ - (0.66, 0.55, 0.44, 0.33, 0.22, 0.11, 0), - (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (0.66, 0.0, 0.44, 0.0, 0.0, 0.0, 0), + (0.1, 0.1, 0.3, 0.0, 0.0, 0.0, 1), (-1, -1, -1, -3, -3, -3, -3), (2, 105, 0, -3, -3, -3, -3), (-1, -1, -2, -2, -2, -2, -2), @@ -529,14 +627,14 @@ def test__step__action_sequence_has_expected_result( True, ], [ - [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.LATTICE_PARAMETERS, Stage.COMPOSITION, [ (np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf), - (0.66, 0.55, 0.44, 0.33, 0.22, 0.11, 0), - (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (0.66, 0.0, 0.44, 0.0, 0.0, 0.0, 0), + (0.1, 0.1, 0.3, 0.0, 0.0, 0.0, 1), (-1, -1, -1, -3, -3, -3, -3), (2, 105, 0, -3, -3, -3, -3), (-1, -1, -2, -2, -2, -2, -2), @@ -566,27 +664,6 @@ def test__step_backwards__action_sequence_has_expected_result( assert valid == last_action_valid -# TODO: Remove if get_parents is removed -@pytest.mark.parametrize( - "actions", - [ - [(1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2)], - [ - (1, 1, -2, -2, -2, -2, -2), - (3, 4, -2, -2, -2, -2, -2), - (-1, -1, -2, -2, -2, -2, -2), - (2, 105, 0, -3, -3, -3, -3), - (-1, -1, -1, -3, -3, -3, -3), - ], - ], -) -def test__get_parents__contains_previous_action_after_a_step(env, actions): - for action in actions: - env.step(action) - parents, parent_actions = env.get_parents() - assert action in parent_actions - - @pytest.mark.parametrize( "actions", [ @@ -607,16 +684,24 @@ def test__reset(env, actions): env.step(action) assert env.state != env.source - for subenv in [env.composition, env.space_group, env.lattice_parameters]: + for subenv in [ + env.subenvs[Stage.COMPOSITION], + env.subenvs[Stage.SPACE_GROUP], + env.subenvs[Stage.LATTICE_PARAMETERS], + ]: assert subenv.state != subenv.source - assert env.lattice_parameters.lattice_system != TRICLINIC + assert env.subenvs[Stage.LATTICE_PARAMETERS].lattice_system != TRICLINIC env.reset() assert env.state == env.source - for subenv in [env.composition, env.space_group, env.lattice_parameters]: + for subenv in [ + env.subenvs[Stage.COMPOSITION], + env.subenvs[Stage.SPACE_GROUP], + env.subenvs[Stage.LATTICE_PARAMETERS], + ]: assert subenv.state == subenv.source - assert env.lattice_parameters.lattice_system == TRICLINIC + assert env.subenvs[Stage.LATTICE_PARAMETERS].lattice_system == TRICLINIC # TODO: write new test of masks, both fw and bw @@ -659,44 +744,50 @@ def test__get_mask_invalid_actions_forward__masks_all_actions_from_different_sta mask = env.get_mask_invalid_actions_forward() if env._get_stage() == Stage.COMPOSITION: - assert not all(mask[: len(env.composition.action_space)]) - assert all(mask[len(env.composition.action_space) :]) + assert not all(mask[: len(env.subenvs[Stage.COMPOSITION].action_space)]) + assert all(mask[len(env.subenvs[Stage.COMPOSITION].action_space) :]) if env._get_stage() == Stage.SPACE_GROUP: assert not all( mask[ - len(env.composition.action_space) : len(env.composition.action_space) - + len(env.space_group.action_space) + len(env.subenvs[Stage.COMPOSITION].action_space) : len( + env.subenvs[Stage.COMPOSITION].action_space + ) + + len(env.subenvs[Stage.SPACE_GROUP].action_space) ] ) - assert all(mask[: len(env.composition.action_space)]) + assert all(mask[: len(env.subenvs[Stage.COMPOSITION].action_space)]) assert all( mask[ - len(env.composition.action_space) + len(env.space_group.action_space) : + len(env.subenvs[Stage.COMPOSITION].action_space) + + len(env.subenvs[Stage.SPACE_GROUP].action_space) : ] ) if env._get_stage() == Stage.LATTICE_PARAMETERS: assert not all( mask[ - len(env.composition.action_space) + len(env.space_group.action_space) : + len(env.subenvs[Stage.COMPOSITION].action_space) + + len(env.subenvs[Stage.SPACE_GROUP].action_space) : ] ) assert all( mask[ - : len(env.composition.action_space) + len(env.space_group.action_space) + : len(env.subenvs[Stage.COMPOSITION].action_space) + + len(env.subenvs[Stage.SPACE_GROUP].action_space) ] ) +@pytest.mark.skip(reason="skip while developping other tests") def test__get_policy_outputs__is_the_concatenation_of_subenvs(env): - policy_output_composition = env.composition.get_policy_output( - env.composition.fixed_distr_params - ) - policy_output_space_group = env.space_group.get_policy_output( - env.space_group.fixed_distr_params + policy_output_composition = env.subenvs[Stage.COMPOSITION].get_policy_output( + env.subenvs[Stage.COMPOSITION].fixed_distr_params ) - policy_output_lattice_parameters = env.lattice_parameters.get_policy_output( - env.lattice_parameters.fixed_distr_params + policy_output_space_group = env.subenvs[Stage.SPACE_GROUP].get_policy_output( + env.subenvs[Stage.SPACE_GROUP].fixed_distr_params ) + policy_output_lattice_parameters = env.subenvs[ + Stage.LATTICE_PARAMETERS + ].get_policy_output(env.subenvs[Stage.LATTICE_PARAMETERS].fixed_distr_params) policy_output_cat = torch.cat( ( policy_output_composition, @@ -711,16 +802,20 @@ def test__get_policy_outputs__is_the_concatenation_of_subenvs(env): def test___get_policy_outputs_of_subenv__returns_correct_output(env): n_states = 5 policy_output_composition = torch.tile( - env.composition.get_policy_output(env.composition.fixed_distr_params), + env.subenvs[Stage.COMPOSITION].get_policy_output( + env.subenvs[Stage.COMPOSITION].fixed_distr_params + ), dims=(n_states, 1), ) policy_output_space_group = torch.tile( - env.space_group.get_policy_output(env.space_group.fixed_distr_params), + env.subenvs[Stage.SPACE_GROUP].get_policy_output( + env.subenvs[Stage.SPACE_GROUP].fixed_distr_params + ), dims=(n_states, 1), ) policy_output_lattice_parameters = torch.tile( - env.lattice_parameters.get_policy_output( - env.lattice_parameters.fixed_distr_params + env.subenvs[Stage.LATTICE_PARAMETERS].get_policy_output( + env.subenvs[Stage.LATTICE_PARAMETERS].fixed_distr_params ), dims=(n_states, 1), ) @@ -747,92 +842,6 @@ def test___get_policy_outputs_of_subenv__returns_correct_output(env): ) -@pytest.mark.parametrize( - "state, state_composition, state_space_group, state_lattice_parameters", - [ - [ - [0, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [1, 0, 4, 0], - [0, 0, 0], - [-1, -1, -1, -1, -1, -1], - ], - [ - [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [0, 0, 0, 0], - [0, 0, 0], - [-1, -1, -1, -1, -1, -1], - ], - [ - [1, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [1, 0, 4, 0], - [0, 0, 0], - [-1, -1, -1, -1, -1, -1], - ], - [ - [1, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], - [1, 0, 4, 0], - [4, 3, 105], - [-1, -1, -1, -1, -1, -1], - ], - [ - [1, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], - [1, 0, 4, 0], - [4, 3, 105], - [-1, -1, -1, -1, -1, -1], - ], - [ - [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], - [1, 0, 4, 0], - [4, 3, 105], - [-1, -1, -1, -1, -1, -1], - ], - [ - [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], - [1, 0, 4, 0], - [4, 3, 105], - [-1, -1, -1, -1, -1, -1], - ], - [ - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], - [1, 0, 4, 0], - [4, 3, 105], - [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], - ], - [ - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], - [1, 0, 4, 0], - [4, 3, 105], - [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], - ], - [ - [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], - [1, 0, 4, 0], - [4, 3, 105], - [0.76, 0.75, 0.74, 0.73, 0.72, 0.71], - ], - [ - [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], - [1, 0, 4, 0], - [4, 3, 105], - [0.76, 0.75, 0.74, 0.73, 0.72, 0.71], - ], - ], -) -def test__state_of_subenv__returns_expected( - env, state, state_composition, state_space_group, state_lattice_parameters -): - for stage in env.subenvs: - state_subenv = env._get_state_of_subenv(state, stage) - if stage == Stage.COMPOSITION: - assert state_subenv == state_composition - elif stage == Stage.SPACE_GROUP: - assert state_subenv == state_space_group - elif stage == Stage.LATTICE_PARAMETERS: - assert state_subenv == state_lattice_parameters - else: - raise ValueError(f"Unrecognized stage {stage}.") - - @pytest.mark.parametrize( "states", [ @@ -895,7 +904,6 @@ def test__step_random__does_not_crash_from_source(env): pass -# @pytest.mark.skip(reason="skip while developping other tests") @pytest.mark.parametrize( "states", [ @@ -917,15 +925,15 @@ def test__step_random__does_not_crash_from_source(env): [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [2, 1, 0, 4, 0, 4, 3, 105, 0.12, 0.23, 0.34, 0.45, 0.56, 0.67], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], - [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [2, 1, 0, 4, 0, 4, 3, 105, 0.5, 0.5, 0.3, 0.4, 0.4, 0.4], + [2, 1, 0, 4, 0, 4, 3, 105, 0.45, 0.45, 0.33, 0.4, 0.4, 0.4], [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], ], ], @@ -936,7 +944,6 @@ def test__sample_actions_forward__returns_valid_actions(env, states): """ n_states = len(states) # Get masks - lens = [len(env.get_mask_invalid_actions_forward(s)) for s in states] masks = tbool( [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device ) @@ -954,6 +961,60 @@ def test__sample_actions_forward__returns_valid_actions(env, states): assert action in env.get_valid_actions(state, done=False, backward=False) +@pytest.mark.parametrize( + "states", + [ + [ + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + [ + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + ], + [ + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.5, 0.5, 0.3, 0.4, 0.4, 0.4], + [2, 1, 0, 4, 0, 4, 3, 105, 0.45, 0.45, 0.33, 0.4, 0.4, 0.4], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + ], +) +def test__sample_actions_backward__returns_valid_actions(env, states): + """ + Just a little higher... + """ + n_states = len(states) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device + ) + # Build policy outputs + params = env.random_distr_params + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Sample actions + actions, _ = env.sample_actions_batch( + policy_outputs, masks, states, is_backward=True + ) + # Sample actions are valid + for state, action in zip(states, actions): + if env._get_stage(state) == Stage.LATTICE_PARAMETERS: + continue + assert action in env.get_valid_actions(state, done=False, backward=True) + + @pytest.mark.repeat(100) def test__trajectory_random__does_not_crash_from_source(env): """ @@ -964,11 +1025,184 @@ def test__trajectory_random__does_not_crash_from_source(env): pass +@pytest.mark.parametrize( + "states, actions", + [ + [ + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + [ + (1, 7, -2, -2, -2, -2, -2), + (3, 16, -2, -2, -2, -2, -2), + (1, 6, -2, -2, -2, -2, -2), + (3, 8, -2, -2, -2, -2, -2), + (2, 11, -2, -2, -2, -2, -2), + (3, 9, -2, -2, -2, -2, -2), + ], + ], + [ + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + ], + [ + (1, 6, -2, -2, -2, -2, -2), + (2, 14, 0, -3, -3, -3, -3), + (2, 2, 1, -3, -3, -3, -3), + (2, 1, 3, -3, -3, -3, -3), + ], + ], + [ + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.5, 0.5, 0.3, 0.4, 0.4, 0.4], + [2, 1, 0, 4, 0, 4, 3, 105, 0.45, 0.45, 0.33, 0.4, 0.4, 0.4], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + [ + (1, 15, -2, -2, -2, -2, -2), + (1, 2, -2, -2, -2, -2, -2), + (2, 7, 0, -3, -3, -3, -3), + (0.49, 0.40, 0.40, 0.37, 0.35, 0.36, 0.0), + (2, 1, 1, -3, -3, -3, -3), + (2, 1, 3, -3, -3, -3, -3), + (2, 11, -2, -2, -2, -2, -2), + (3, 9, -2, -2, -2, -2, -2), + (2, 2, 3, -3, -3, -3, -3), + (3, 2, -2, -2, -2, -2, -2), + (0.27, 0.28, 0.30, 0.39, 0.37, 0.29, 0.0), + (0.32, 0.30, 0.45, 0.33, 0.42, 0.39, 0.0), + (4, 4, -2, -2, -2, -2, -2), + ], + ], + ], +) +def test__get_logprobs_forward__returns_valid_actions(env, states, actions): + """ + This would already be not too bad! + """ + n_states = len(states) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_forward(s) for s in states], device=env.device + ) + # Build policy outputs + params = env.random_distr_params + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states, is_backward=False + ) + assert torch.all(torch.isfinite(logprobs)) + + +# Set lattice system +@pytest.mark.parametrize( + "states, actions", + [ + [ + [ + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + [ + (2, 4, -2, -2, -2, -2, -2), + (2, 4, -2, -2, -2, -2, -2), + (1, 3, -2, -2, -2, -2, -2), + (1, 3, -2, -2, -2, -2, -2), + (4, 6, -2, -2, -2, -2, -2), + ], + ], + [ + [ + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + ], + [ + (-1, -1, -2, -2, -2, -2, -2), + (0, 1, 0, -3, -3, -3, -3), + (1, 1, 1, -3, -3, -3, -3), + ], + ], + [ + [ + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + # [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + # [2, 1, 0, 4, 0, 4, 3, 105, 0.5, 0.5, 0.3, 0.4, 0.4, 0.4], + # [2, 1, 0, 4, 0, 4, 3, 105, 0.45, 0.45, 0.33, 0.4, 0.4, 0.4], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + [ + (2, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + # (0.10, 0.10, 0.17, 0.0, 0.0, 0.0, 0.0), + (0, 1, 0, -3, -3, -3, -3), + (1, 1, 1, -3, -3, -3, -3), + (1, 3, -2, -2, -2, -2, -2), + (1, 3, -2, -2, -2, -2, -2), + (1, 2, 1, -3, -3, -3, -3), + (2, 1, -2, -2, -2, -2, -2), + # (0.37, 0.37, 0.23, 0.0, 0.0, 0.0, 0.0), + # (0.23, 0.23, 0.11, 0.0, 0.0, 0.0, 0.0), + (3, 3, -2, -2, -2, -2, -2), + ], + ], + ], +) +def test__get_logprobs_backward__returns_valid_actions(env, states, actions): + """ + And backwards? + """ + n_states = len(states) + actions = tfloat(actions, float_type=env.float, device=env.device) + # Get masks + masks = tbool( + [env.get_mask_invalid_actions_backward(s) for s in states], device=env.device + ) + # Build policy outputs + params = env.random_distr_params + policy_outputs = torch.tile(env.get_policy_output(params), dims=(n_states, 1)) + # Get log probs + logprobs = env.get_logprobs( + policy_outputs, actions, masks, states, is_backward=True + ) + assert torch.all(torch.isfinite(logprobs)) + + @pytest.mark.skip(reason="skip until updated") def test__continuous_env_common(env): return common.test__all_env_common(env) -@pytest.mark.skip(reason="skip until updated") -def test__all_env_common(env_with_stoichiometry_sg_check): - return common.test__all_env_common(env_with_stoichiometry_sg_check) +# @pytest.mark.skip(reason="skip until updated") +# def test__all_env_common(env_with_stoichiometry_sg_check): +# return common.test__all_env_common(env_with_stoichiometry_sg_check) From ea66d5267a80db457427484930e6acc34e3ea087 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 21:39:01 -0400 Subject: [PATCH 071/205] Fix in cube: entire actions were passed to self._mask_ignored_dimensions instead of just the dimensions part --- gflownet/envs/cube.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index a5a9df230..020ef8c70 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -950,8 +950,8 @@ def _sample_actions_batch_backward( ) actions_tensor[is_bts] = actions_bts # Make ignored dimensions zero - actions_tensor[is_bts] = self._mask_ignored_dimensions( - mask[is_bts], actions_tensor[is_bts] + actions_tensor[is_bts, :-1] = self._mask_ignored_dimensions( + mask[is_bts], actions_tensor[is_bts, :-1] ) actions = [tuple(a.tolist()) for a in actions_tensor] return actions, None From bafab0e4af4bb72b0c56f5abc4120879f4d90cc5 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 21:40:51 -0400 Subject: [PATCH 072/205] Minor changes in common tests --- tests/gflownet/envs/common.py | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/tests/gflownet/envs/common.py b/tests/gflownet/envs/common.py index 1bac1ca3a..1d8338b14 100644 --- a/tests/gflownet/envs/common.py +++ b/tests/gflownet/envs/common.py @@ -330,7 +330,7 @@ def test__forward_actions_have_nonzero_backward_prob(env): tfloat(env.random_policy_output, float_type=env.float, device=env.device), 0 ) while not env.done: - state_new, action, valid = env.step_random(backward=False) + state_next, action, valid = env.step_random(backward=False) if not valid: continue # Get backward logprobs @@ -339,19 +339,17 @@ def test__forward_actions_have_nonzero_backward_prob(env): actions_torch = torch.unsqueeze( tfloat(action, float_type=env.float, device=env.device), 0 ) - states_torch = torch.unsqueeze( - tfloat(env.state, float_type=env.float, device=env.device), 0 - ) policy_outputs = policy_random.clone().detach() logprobs_bw = env.get_logprobs( policy_outputs=policy_outputs, actions=actions_torch, mask=masks, - states_from=states_torch, + states_from=[env.state], is_backward=True, ) assert torch.isfinite(logprobs_bw) assert logprobs_bw > -1e6 + state_prev = copy(state_next) @pytest.mark.repeat(1000) @@ -398,7 +396,7 @@ def test__backward_actions_have_nonzero_forward_prob(env, n=1000): while True: if env.equal(env.state, env.source): break - state_new, action, valid = env.step_random(backward=True) + state_next, action, valid = env.step_random(backward=True) assert valid # Get forward logprobs mask_fw = env.get_mask_invalid_actions_forward() @@ -406,19 +404,17 @@ def test__backward_actions_have_nonzero_forward_prob(env, n=1000): actions_torch = torch.unsqueeze( tfloat(action, float_type=env.float, device=env.device), 0 ) - states_torch = torch.unsqueeze( - tfloat(env.state, float_type=env.float, device=env.device), 0 - ) policy_outputs = policy_random.clone().detach() logprobs_fw = env.get_logprobs( policy_outputs=policy_outputs, actions=actions_torch, mask=masks, - states_from=states_torch, + states_from=[env.state], is_backward=False, ) assert torch.isfinite(logprobs_fw) assert logprobs_fw > -1e6 + state_prev = copy(state_next) @pytest.mark.repeat(10) From f47f03c9acc1f86e262d83525397291a01cc7d6e Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 21:41:21 -0400 Subject: [PATCH 073/205] Uncomment common tests --- tests/gflownet/envs/test_ccrystal.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index c0d411a95..4bc6b6fc1 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -1114,7 +1114,7 @@ def test__get_logprobs_forward__returns_valid_actions(env, states, actions): assert torch.all(torch.isfinite(logprobs)) -# Set lattice system +# TODO: Set lattice system @pytest.mark.parametrize( "states, actions", [ @@ -1198,9 +1198,8 @@ def test__get_logprobs_backward__returns_valid_actions(env, states, actions): assert torch.all(torch.isfinite(logprobs)) -@pytest.mark.skip(reason="skip until updated") def test__continuous_env_common(env): - return common.test__all_env_common(env) + return common.test__continuous_env_common(env) # @pytest.mark.skip(reason="skip until updated") From ce3f4a500777efba4ba3cf5b23d378bae1ca5cbb Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 21:43:01 -0400 Subject: [PATCH 074/205] Add dummy test for debugging --- tests/gflownet/envs/test_spacegroup.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/tests/gflownet/envs/test_spacegroup.py b/tests/gflownet/envs/test_spacegroup.py index 50e82d61c..78df44dc1 100644 --- a/tests/gflownet/envs/test_spacegroup.py +++ b/tests/gflownet/envs/test_spacegroup.py @@ -8,6 +8,7 @@ from gflownet.envs.crystals.spacegroup import SpaceGroup N_ATOMS = [3, 7, 9] +N_ATOMS_B = [5, 0, 14, 1] SG_SUBSET = [1, 17, 39, 123, 230] @@ -21,6 +22,11 @@ def env_with_composition(): return SpaceGroup(n_atoms=N_ATOMS) +@pytest.fixture +def env_with_composition_b(): + return SpaceGroup(n_atoms=N_ATOMS_B) + + @pytest.fixture def env_with_restricted_spacegroups(): return SpaceGroup(space_groups_subset=SG_SUBSET) @@ -52,6 +58,11 @@ def test__environment__action_space_has_eos(): assert env.eos in env.action_space +def test__env_with_composition_b__debug(env_with_composition_b): + env = env_with_composition_b + pass + + @pytest.mark.parametrize( "action, expected", [ From 335cecbae2f4933c616cecae82e5ce27b9acf008 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Sun, 24 Sep 2023 23:57:07 -0400 Subject: [PATCH 075/205] add exp file --- config/experiments/workshop23/matbench.yaml | 69 +++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 config/experiments/workshop23/matbench.yaml diff --git a/config/experiments/workshop23/matbench.yaml b/config/experiments/workshop23/matbench.yaml new file mode 100644 index 000000000..1027b4bf2 --- /dev/null +++ b/config/experiments/workshop23/matbench.yaml @@ -0,0 +1,69 @@ +# @package _global_ + +defaults: + - override /env: crystals/crystal + - override /gflownet: trajectorybalance + - override /proxy: crystals/dave + - override /logger: wandb + +device: cpu + +# Environment +env: + lattice_parameters_kwargs: + min_length: 1.0 + max_length: 350.0 + min_angle: 50.0 + max_angle: 150.0 + grid_size: 20 + composition_kwargs: + elements: [1,3,4,5,6,7,8,9,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,89,90,91,92,93,94] + reward_func: boltzmann + reward_beta: 1 + +# GFlowNet hyperparameters +gflownet: + random_action_prob: 0.1 + optimizer: + batch_size: + forward: 10 + lr: 0.001 + z_dim: 16 + lr_z_mult: 100 + n_train_steps: 40000 + lr_decay_period: 1000000 + policy: + forward: + type: mlp + n_hid: 512 + n_layers: 5 + checkpoint: forward + backward: + type: mlp + n_hid: 512 + n_layers: 5 + shared_weights: False + checkpoint: backward + +# WandB +logger: + lightweight: True + project_name: "crystal-gfn" + tags: + - gflownet + - crystals + - matbench + - workshop23 + checkpoints: + period: 500 + do: + online: true + test: + n_top_k: 5000 + top_k: 100 + top_k_period: -1 + +# Hydra +hydra: + run: + dir: ${user.logdir.root}/workshop23/matbench/${oc.env:SLURM_JOB_ID,local}/${now:%Y-%m-%d_%H-%M-%S} From 2fb1a422ccf30dcba224fde1f82b536a7b426842 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 25 Sep 2023 11:56:36 -0400 Subject: [PATCH 076/205] Fix spacegroup _is_compatible() --- gflownet/envs/crystals/spacegroup.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/spacegroup.py b/gflownet/envs/crystals/spacegroup.py index 865e146c9..83713ddc0 100644 --- a/gflownet/envs/crystals/spacegroup.py +++ b/gflownet/envs/crystals/spacegroup.py @@ -624,7 +624,9 @@ def _is_compatible( False otherwise. """ # Get list of space groups compatible with the composition - space_groups = [self.n_atoms_compatibility_dict[sg] for sg in self.space_groups] + space_groups = [ + sg for sg in self.space_groups if self.n_atoms_compatibility_dict[sg] + ] # Prune the list of space groups to those compatible with the provided crystal- # lattice system From 1b27b67f2fad71f2309db8643cdc5450f44db417 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 12:46:53 -0400 Subject: [PATCH 077/205] Enable new and extend continuous crystal tests. --- tests/gflownet/envs/test_ccrystal.py | 35 +++++++++++++++++++++++++--- 1 file changed, 32 insertions(+), 3 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 4bc6b6fc1..3562c1447 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -10,6 +10,29 @@ from gflownet.envs.crystals.clattice_parameters import TRICLINIC from gflownet.utils.common import tbool, tfloat +SG_SUBSET_ALL_CLS_PS = [ + 1, + 2, + 3, + 6, + 16, + 17, + 67, + 81, + 89, + 127, + 143, + 144, + 146, + 148, + 168, + 169, + 189, + 195, + 200, + 230, +] + @pytest.fixture def env(): @@ -24,6 +47,7 @@ def env_with_stoichiometry_sg_check(): return CCrystal( composition_kwargs={"elements": 4}, do_stoichiometry_sg_check=True, + space_group_kwargs={"space_groups_subset": SG_SUBSET_ALL_CLS_PS}, ) @@ -1199,9 +1223,14 @@ def test__get_logprobs_backward__returns_valid_actions(env, states, actions): def test__continuous_env_common(env): + print( + "\n\nCommon tests for crystal without composition <-> space group constraints\n" + ) return common.test__continuous_env_common(env) -# @pytest.mark.skip(reason="skip until updated") -# def test__all_env_common(env_with_stoichiometry_sg_check): -# return common.test__all_env_common(env_with_stoichiometry_sg_check) +def test__continuous_env_with_stoichiometry_sg_check_common( + env_with_stoichiometry_sg_check, +): + print("\n\nCommon tests for crystal with composition <-> space group constraints\n") + return common.test__continuous_env_common(env_with_stoichiometry_sg_check) From accd7617c8f4b327f7494483e43713d5cfdb4c9b Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 14:53:55 -0400 Subject: [PATCH 078/205] update params from discussion with Alex --- config/experiments/workshop23/matbench.yaml | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/config/experiments/workshop23/matbench.yaml b/config/experiments/workshop23/matbench.yaml index 1027b4bf2..3e87b96a2 100644 --- a/config/experiments/workshop23/matbench.yaml +++ b/config/experiments/workshop23/matbench.yaml @@ -15,23 +15,27 @@ env: max_length: 350.0 min_angle: 50.0 max_angle: 150.0 - grid_size: 20 + grid_size: 10 composition_kwargs: elements: [1,3,4,5,6,7,8,9,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,89,90,91,92,93,94] - reward_func: boltzmann - reward_beta: 1 + reward_func: identity # or boltzmann or -> "if self.denorm_proxy:" + reward_beta: 1 # try 10 20 if boltzmann + buffer: + replay_capacity: 0 # GFlowNet hyperparameters gflownet: - random_action_prob: 0.1 + random_action_prob: 0.1 # try 0.0 optimizer: batch_size: forward: 10 - lr: 0.001 + backward_replay: -1 + lr: 0.001 # explore this z_dim: 16 lr_z_mult: 100 - n_train_steps: 40000 + n_train_steps: 10000 lr_decay_period: 1000000 + replay_sampling: weighted policy: forward: type: mlp @@ -45,6 +49,9 @@ gflownet: shared_weights: False checkpoint: backward +# also replay buffer +# proxy uniform + # WandB logger: lightweight: True @@ -59,6 +66,8 @@ logger: do: online: true test: + period: -1 + n: 500 n_top_k: 5000 top_k: 100 top_k_period: -1 From 1e59abcc46979223c268073df6c18b84df2c5fc6 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 25 Sep 2023 11:56:36 -0400 Subject: [PATCH 079/205] Fix spacegroup _is_compatible() --- gflownet/envs/crystals/spacegroup.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/spacegroup.py b/gflownet/envs/crystals/spacegroup.py index 5e7b25810..0bd8776dd 100644 --- a/gflownet/envs/crystals/spacegroup.py +++ b/gflownet/envs/crystals/spacegroup.py @@ -609,7 +609,9 @@ def _is_compatible( False otherwise. """ # Get list of space groups compatible with the composition - space_groups = [self.n_atoms_compatibility_dict[sg] for sg in self.space_groups] + space_groups = [ + sg for sg in self.space_groups if self.n_atoms_compatibility_dict[sg] + ] # Prune the list of space groups to those compatible with the provided crystal- # lattice system From 41d765925a7bc9bfdad696031c5ede0a7eff6b7c Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 14:55:20 -0400 Subject: [PATCH 080/205] default boltzmann --- config/experiments/workshop23/matbench.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/experiments/workshop23/matbench.yaml b/config/experiments/workshop23/matbench.yaml index 3e87b96a2..6167ebd1f 100644 --- a/config/experiments/workshop23/matbench.yaml +++ b/config/experiments/workshop23/matbench.yaml @@ -18,7 +18,7 @@ env: grid_size: 10 composition_kwargs: elements: [1,3,4,5,6,7,8,9,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,89,90,91,92,93,94] - reward_func: identity # or boltzmann or -> "if self.denorm_proxy:" + reward_func: boltzmann # or identity or boltzmann or -> "if self.denorm_proxy:" reward_beta: 1 # try 10 20 if boltzmann buffer: replay_capacity: 0 From 77a9c32a4843f1dd5fc1f89d90cf742f5bcdd524 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 14:58:38 -0400 Subject: [PATCH 081/205] handle logger notes in wandb --- gflownet/utils/logger.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gflownet/utils/logger.py b/gflownet/utils/logger.py index e9556f9c5..50356b377 100644 --- a/gflownet/utils/logger.py +++ b/gflownet/utils/logger.py @@ -32,6 +32,7 @@ def __init__( run_name=None, tags: list = None, context: str = "0", + notes: str = None, ): self.config = config self.do = do @@ -60,7 +61,7 @@ def __init__( if slurm_job_id: wandb_config["slurm_job_id"] = slurm_job_id self.run = self.wandb.init( - config=wandb_config, project=project_name, name=run_name + config=wandb_config, project=project_name, name=run_name, notes=notes ) else: self.wandb = None From 3813bcc27e38babca967600e9e60f6936f9072fc Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 14:59:39 -0400 Subject: [PATCH 082/205] describe notes config --- config/logger/base.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/config/logger/base.yaml b/config/logger/base.yaml index bc95f20d6..640167c81 100644 --- a/config/logger/base.yaml +++ b/config/logger/base.yaml @@ -44,3 +44,4 @@ debug: False lightweight: False progress: True context: "0" +notes: null # wandb run notes (e.g. "baseline") From 2444d17885c62212bd2a0be4bba8df64874d392d Mon Sep 17 00:00:00 2001 From: michalkoziarski Date: Mon, 25 Sep 2023 15:10:19 -0400 Subject: [PATCH 083/205] _get_param fix --- gflownet/envs/crystals/clattice_parameters.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index da2eed32e..beca1bd47 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -98,17 +98,17 @@ def _statevalue2angle(self, value): def _angle2statevalue(self, angle): return (angle - self.min_angle) / self.angle_range - def _get_param(self, param): + def _get_param(self, state, param): if hasattr(self, param): return getattr(self, param) else: if param in LENGTH_PARAMETER_NAMES: return self._statevalue2length( - self.state[self._get_index_of_param(param)] + state[self._get_index_of_param(param)] ) elif param in ANGLE_PARAMETER_NAMES: return self._statevalue2angle( - self.state[self._get_index_of_param(param)] + state[self._get_index_of_param(param)] ) else: raise ValueError(f"{param} is not a valid lattice parameter") @@ -241,7 +241,7 @@ def _unpack_lengths_angles( """ state = self._get_state(state) - a, b, c, alpha, beta, gamma = [self._get_param(p) for p in PARAMETER_NAMES] + a, b, c, alpha, beta, gamma = [self._get_param(state, p) for p in PARAMETER_NAMES] return (a, b, c), (alpha, beta, gamma) def state2readable(self, state: Optional[List[int]] = None) -> str: From d06024a5e41a4846949d8819afcdee3c60d9ec72 Mon Sep 17 00:00:00 2001 From: michalkoziarski Date: Mon, 25 Sep 2023 15:10:47 -0400 Subject: [PATCH 084/205] removed unused imports --- gflownet/envs/crystals/clattice_parameters.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index beca1bd47..ba6574d32 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -3,18 +3,13 @@ """ from typing import List, Optional, Tuple -import numpy as np -import torch -from torch import Tensor from torchtyping import TensorType -from gflownet.envs.crystals.lattice_parameters import LatticeParameters from gflownet.envs.cube import ContinuousCube from gflownet.utils.common import copy, tfloat from gflownet.utils.crystals.constants import ( CUBIC, HEXAGONAL, - LATTICE_SYSTEMS, MONOCLINIC, ORTHORHOMBIC, RHOMBOHEDRAL, From 802f218909af50450fa863a80435489a67b26d64 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 15:14:08 -0400 Subject: [PATCH 085/205] rename to `discrete-matbench` --- .../workshop23/{matbench.yaml => discrete-matbench.yaml} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename config/experiments/workshop23/{matbench.yaml => discrete-matbench.yaml} (100%) diff --git a/config/experiments/workshop23/matbench.yaml b/config/experiments/workshop23/discrete-matbench.yaml similarity index 100% rename from config/experiments/workshop23/matbench.yaml rename to config/experiments/workshop23/discrete-matbench.yaml From 4b4cd910db06a7b608dee5840b25312ba4e606a6 Mon Sep 17 00:00:00 2001 From: michalkoziarski Date: Mon, 25 Sep 2023 15:14:14 -0400 Subject: [PATCH 086/205] state2oracle methods (in progress) --- gflownet/envs/crystals/clattice_parameters.py | 47 +++++++++++++++++++ 1 file changed, 47 insertions(+) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index ba6574d32..ad3124fb5 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -3,6 +3,7 @@ """ from typing import List, Optional, Tuple +from torch import Tensor from torchtyping import TensorType from gflownet.envs.cube import ContinuousCube @@ -275,3 +276,49 @@ def statebatch2proxy( return self.statetorch2proxy( tfloat(states, float_type=self.float, device=self.device) ) + + def state2oracle(self, state: Optional[List[int]] = None) -> Tensor: + """ + Prepares a list of states in "GFlowNet format" for the oracle. + + Args + ---- + state : list + A state. + + Returns + ---- + oracle_state : Tensor + Tensor containing lengths and angles converted from the Grid format. + """ + if state is None: + state = self.state.copy() + + return Tensor( + [self.cell2length[s] for s in state[:3]] + + [self.cell2angle[s] for s in state[3:]] + ) + + def statetorch2oracle( + self, states: TensorType["batch", "state_dim"] + ) -> TensorType["batch", "state_oracle_dim"]: + """ + Prepares a batch of states in "GFlowNet format" for the oracle. The input to the + oracle is the lengths and angles. + + Args + ---- + states : Tensor + A state + + Returns + ---- + oracle_states : Tensor + """ + return torch.cat( + [ + self.lengths_tensor[states[:, :3].long()], + self.angles_tensor[states[:, 3:].long()], + ], + dim=1, + ) From c69d6af424e57c23c434510c39120f7d95ab2b65 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 15:16:21 -0400 Subject: [PATCH 087/205] update hydra run dir --- .../experiments/workshop23/discrete-matbench.yaml | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/config/experiments/workshop23/discrete-matbench.yaml b/config/experiments/workshop23/discrete-matbench.yaml index 6167ebd1f..7816d2e51 100644 --- a/config/experiments/workshop23/discrete-matbench.yaml +++ b/config/experiments/workshop23/discrete-matbench.yaml @@ -18,19 +18,19 @@ env: grid_size: 10 composition_kwargs: elements: [1,3,4,5,6,7,8,9,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,89,90,91,92,93,94] - reward_func: boltzmann # or identity or boltzmann or -> "if self.denorm_proxy:" - reward_beta: 1 # try 10 20 if boltzmann + reward_func: boltzmann + reward_beta: 1 buffer: replay_capacity: 0 # GFlowNet hyperparameters gflownet: - random_action_prob: 0.1 # try 0.0 + random_action_prob: 0.1 optimizer: batch_size: forward: 10 backward_replay: -1 - lr: 0.001 # explore this + lr: 0.001 z_dim: 16 lr_z_mult: 100 n_train_steps: 10000 @@ -49,9 +49,6 @@ gflownet: shared_weights: False checkpoint: backward -# also replay buffer -# proxy uniform - # WandB logger: lightweight: True @@ -75,4 +72,4 @@ logger: # Hydra hydra: run: - dir: ${user.logdir.root}/workshop23/matbench/${oc.env:SLURM_JOB_ID,local}/${now:%Y-%m-%d_%H-%M-%S} + dir: ${user.logdir.root}/workshop23/discrete-matbench/${oc.env:SLURM_JOB_ID,local}/${now:%Y-%m-%d_%H-%M-%S} From dbe9cf5dc5af1e7af6e0f8b33c9868ffc5d93403 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 15:59:19 -0400 Subject: [PATCH 088/205] handle per-job git repo with `--code_dir='$SLURM_TMPDIR'` --- LAUNCH.md | 10 +++++++-- mila/launch.py | 58 +++++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 65 insertions(+), 3 deletions(-) diff --git a/LAUNCH.md b/LAUNCH.md index 9d5aa5bd0..074dfe3f8 100644 --- a/LAUNCH.md +++ b/LAUNCH.md @@ -9,8 +9,8 @@ usage: launch.py [-h] [--help-md] [--job_name JOB_NAME] [--outdir OUTDIR] [--cpus_per_task CPUS_PER_TASK] [--mem MEM] [--gres GRES] [--partition PARTITION] [--modules MODULES] [--conda_env CONDA_ENV] [--venv VENV] [--template TEMPLATE] - [--code_dir CODE_DIR] [--jobs JOBS] [--dry-run] [--verbose] - [--force] + [--code_dir CODE_DIR] [--git_checkout GIT_CHECKOUT] + [--jobs JOBS] [--dry-run] [--verbose] [--force] optional arguments: -h, --help show this help message and exit @@ -35,6 +35,11 @@ optional arguments: $root/mila/sbatch/template-conda.sh --code_dir CODE_DIR cd before running main.py (defaults to here). Defaults to $root + --git_checkout GIT_CHECKOUT + Branch or commit to checkout before running the code. + This is only used if --code_dir='$SLURM_TMPDIR'. If + not specified, the current branch is used. Defaults to + None --jobs JOBS jobs (nested) file name in external/jobs (with or without .yaml). Or an absolute path to a yaml file anywhere Defaults to None @@ -54,6 +59,7 @@ conda_env : gflownet cpus_per_task : 2 dry-run : False force : False +git_checkout : None gres : gpu:1 job_name : gflownet jobs : None diff --git a/mila/launch.py b/mila/launch.py index b1aa0f227..a02bb8fda 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -7,11 +7,14 @@ from os.path import expandvars from pathlib import Path from textwrap import dedent +from git import Repo from yaml import safe_load ROOT = Path(__file__).resolve().parent.parent +DIRTY_REPO_OK = False + HELP = dedent( """ ## 🥳 User guide @@ -337,6 +340,46 @@ def print_md_help(parser, defaults): print(HELP, end="") +def code_dir_for_slurm_tmp_dir_checkout(git_checkout): + global DIRTY_REPO_OK + + repo = Repo(ROOT) + if git_checkout is None: + git_checkout = repo.active_branch.name + if not DIRTY_REPO_OK: + print("💥 Git warnings:") + print( + f" • `git_checkout` not provided. Using current branch: {git_checkout}" + ) + # warn for uncommitted changes + if repo.is_dirty() and not DIRTY_REPO_OK: + print( + " • Your repo contains uncommitted changes. " + + "They will *not* be available when cloning happens within the job." + ) + if ( + "y" + not in input( + "Continue anyway, ignoring current changes? [y/N] " + ).lower() + ): + print("🛑 Aborted") + sys.exit(0) + DIRTY_REPO_OK = True + + return dedent( + """\ + $SLURM_TMPDIR + git clone {git_url} tpm-gflownet + cd tpm-gflownet + {git_checkout} + """ + ).format( + git_url=repo.remotes.origin.url, + git_checkout=f"git checkout {git_checkout}" if git_checkout else "", + ) + + if __name__ == "__main__": defaults = { "code_dir": "$root", @@ -344,6 +387,7 @@ def print_md_help(parser, defaults): "cpus_per_task": 2, "dry-run": False, "force": False, + "git_checkout": None, "gres": "gpu:1", "job_name": "gflownet", "jobs": None, @@ -428,6 +472,14 @@ def print_md_help(parser, defaults): help="cd before running main.py (defaults to here)." + f" Defaults to {defaults['code_dir']}", ) + parser.add_argument( + "--git_checkout", + type=str, + help="Branch or commit to checkout before running the code." + + " This is only used if --code_dir='$SLURM_TMPDIR'. If not specified, " + + " the current branch is used." + + f" Defaults to {defaults['git_checkout']}", + ) parser.add_argument( "--jobs", type=str, @@ -510,7 +562,11 @@ def print_md_help(parser, defaults): job_args = deep_update(job_args, job_dict) job_args = deep_update(job_args, args) - job_args["code_dir"] = str(resolve(job_args["code_dir"])) + job_args["code_dir"] = ( + str(resolve(job_args["code_dir"])) + if "SLURM_TMPDIR" not in job_args["code_dir"] + else code_dir_for_slurm_tmp_dir_checkout(job_args.get("git_checkout")) + ) job_args["outdir"] = str(resolve(job_args["outdir"])) job_args["venv"] = str(resolve(job_args["venv"])) job_args["main_args"] = script_dict_to_main_args_str(job_args.get("script", {})) From 8b64e698111fe8f1c47680f864e34b1104da9c0a Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 16:03:07 -0400 Subject: [PATCH 089/205] user confirmation even for just for missing `git_checkout` --- mila/launch.py | 21 ++++++++------------- 1 file changed, 8 insertions(+), 13 deletions(-) diff --git a/mila/launch.py b/mila/launch.py index a02bb8fda..0e5899da0 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -13,7 +13,7 @@ ROOT = Path(__file__).resolve().parent.parent -DIRTY_REPO_OK = False +GIT_WARNING = False HELP = dedent( """ @@ -341,31 +341,26 @@ def print_md_help(parser, defaults): def code_dir_for_slurm_tmp_dir_checkout(git_checkout): - global DIRTY_REPO_OK + global GIT_WARNING repo = Repo(ROOT) if git_checkout is None: git_checkout = repo.active_branch.name - if not DIRTY_REPO_OK: + if not GIT_WARNING: print("💥 Git warnings:") print( f" • `git_checkout` not provided. Using current branch: {git_checkout}" ) # warn for uncommitted changes - if repo.is_dirty() and not DIRTY_REPO_OK: + if repo.is_dirty() and not GIT_WARNING: print( " • Your repo contains uncommitted changes. " + "They will *not* be available when cloning happens within the job." ) - if ( - "y" - not in input( - "Continue anyway, ignoring current changes? [y/N] " - ).lower() - ): - print("🛑 Aborted") - sys.exit(0) - DIRTY_REPO_OK = True + if "y" not in input("Continue anyway? [y/N] ").lower(): + print("🛑 Aborted") + sys.exit(0) + GIT_WARNING = True return dedent( """\ From 22d5519ef85bbb62f59acc94dd9026278b6e7365 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 16:15:21 -0400 Subject: [PATCH 090/205] improve git warning logic --- mila/launch.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/mila/launch.py b/mila/launch.py index 0e5899da0..1177ac9af 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -13,7 +13,7 @@ ROOT = Path(__file__).resolve().parent.parent -GIT_WARNING = False +GIT_WARNING = True HELP = dedent( """ @@ -346,21 +346,21 @@ def code_dir_for_slurm_tmp_dir_checkout(git_checkout): repo = Repo(ROOT) if git_checkout is None: git_checkout = repo.active_branch.name - if not GIT_WARNING: + if GIT_WARNING: print("💥 Git warnings:") print( f" • `git_checkout` not provided. Using current branch: {git_checkout}" ) # warn for uncommitted changes - if repo.is_dirty() and not GIT_WARNING: + if repo.is_dirty() and GIT_WARNING: print( " • Your repo contains uncommitted changes. " + "They will *not* be available when cloning happens within the job." ) - if "y" not in input("Continue anyway? [y/N] ").lower(): + if GIT_WARNING and "y" not in input("Continue anyway? [y/N] ").lower(): print("🛑 Aborted") sys.exit(0) - GIT_WARNING = True + GIT_WARNING = False return dedent( """\ @@ -368,6 +368,7 @@ def code_dir_for_slurm_tmp_dir_checkout(git_checkout): git clone {git_url} tpm-gflownet cd tpm-gflownet {git_checkout} + echo "Current commit: $(git rev-parse HEAD)" """ ).format( git_url=repo.remotes.origin.url, From 2cc0e80675e5872bd1756ca32e0ff5a3aed59667 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 14:58:38 -0400 Subject: [PATCH 091/205] handle logger notes in wandb --- gflownet/utils/logger.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gflownet/utils/logger.py b/gflownet/utils/logger.py index e9556f9c5..50356b377 100644 --- a/gflownet/utils/logger.py +++ b/gflownet/utils/logger.py @@ -32,6 +32,7 @@ def __init__( run_name=None, tags: list = None, context: str = "0", + notes: str = None, ): self.config = config self.do = do @@ -60,7 +61,7 @@ def __init__( if slurm_job_id: wandb_config["slurm_job_id"] = slurm_job_id self.run = self.wandb.init( - config=wandb_config, project=project_name, name=run_name + config=wandb_config, project=project_name, name=run_name, notes=notes ) else: self.wandb = None From 0158c43d04239d2350277398dc25784e8cff99b6 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 14:59:39 -0400 Subject: [PATCH 092/205] describe notes config --- config/logger/base.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/config/logger/base.yaml b/config/logger/base.yaml index bc95f20d6..640167c81 100644 --- a/config/logger/base.yaml +++ b/config/logger/base.yaml @@ -44,3 +44,4 @@ debug: False lightweight: False progress: True context: "0" +notes: null # wandb run notes (e.g. "baseline") From 7e98702d80c5b43abe8f2cb6eaa588fe9b40e046 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 16:31:38 -0400 Subject: [PATCH 093/205] handle quotes in generated command-line --- mila/launch.py | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/mila/launch.py b/mila/launch.py index 1177ac9af..cb5950d67 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -265,6 +265,18 @@ def find_jobs_conf(args): return jobs_conf_path, local_out_dir +def quote(value): + v = str(value) + if " " in v: + if "'" not in v: + v = f"'{v}'" + elif '"' not in v: + v = f'"{v}"' + else: + raise ValueError(f"Cannot quote {value}") + return v + + def script_dict_to_main_args_str(script_dict, is_first=True, nested_key=""): """ Recursively turns a dict of script args into a string of main.py args @@ -275,11 +287,14 @@ def script_dict_to_main_args_str(script_dict, is_first=True, nested_key=""): previous_str (str, optional): base string to append to. Defaults to "". """ if not isinstance(script_dict, dict): - return nested_key + "=" + str(script_dict) + " " + return f"{nested_key}={quote(script_dict)} " new_str = "" for k, v in script_dict.items(): if k == "__value__": - new_str += nested_key + "=" + str(v) + " " + value = str(v) + if " " in value: + value = f"'{value}'" + new_str += f"{nested_key}={quote(v)} " continue new_key = k if not nested_key else nested_key + "." + str(k) new_str += script_dict_to_main_args_str(v, nested_key=new_key, is_first=False) From b10810add8ead4ce63316572c4a6bc0307aac850 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 16:31:38 -0400 Subject: [PATCH 094/205] handle quotes in generated command-line --- mila/launch.py | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/mila/launch.py b/mila/launch.py index b1aa0f227..19ba41cd2 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -262,6 +262,18 @@ def find_jobs_conf(args): return jobs_conf_path, local_out_dir +def quote(value): + v = str(value) + if " " in v: + if "'" not in v: + v = f"'{v}'" + elif '"' not in v: + v = f'"{v}"' + else: + raise ValueError(f"Cannot quote {value}") + return v + + def script_dict_to_main_args_str(script_dict, is_first=True, nested_key=""): """ Recursively turns a dict of script args into a string of main.py args @@ -272,11 +284,14 @@ def script_dict_to_main_args_str(script_dict, is_first=True, nested_key=""): previous_str (str, optional): base string to append to. Defaults to "". """ if not isinstance(script_dict, dict): - return nested_key + "=" + str(script_dict) + " " + return f"{nested_key}={quote(script_dict)} " new_str = "" for k, v in script_dict.items(): if k == "__value__": - new_str += nested_key + "=" + str(v) + " " + value = str(v) + if " " in value: + value = f"'{value}'" + new_str += f"{nested_key}={quote(v)} " continue new_key = k if not nested_key else nested_key + "." + str(k) new_str += script_dict_to_main_args_str(v, nested_key=new_key, is_first=False) From 7bbd17af56e549d7971604781e6eba31ff5ad60f Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 16:38:16 -0400 Subject: [PATCH 095/205] strip output for no new line --- mila/launch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mila/launch.py b/mila/launch.py index cb5950d67..f35dcc5de 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -611,7 +611,7 @@ def code_dir_for_slurm_tmp_dir_checkout(git_checkout): sbatch_path.write_text(templated) print(f" 🏷 Created ./{sbatch_path.relative_to(Path.cwd())}") # Submit job to SLURM - out = popen(f"sbatch {sbatch_path}").read() + out = popen(f"sbatch {sbatch_path}").read().strip() # Identify printed-out job id job_id = re.findall(r"Submitted batch job (\d+)", out)[0] job_ids.append(job_id) From c1b489aaf967e72ab4fbfde12c60ee1adf981e2e Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 16:51:54 -0400 Subject: [PATCH 096/205] fix quotes and ssh to https --- mila/launch.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/mila/launch.py b/mila/launch.py index f35dcc5de..6af1afd0a 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -267,6 +267,7 @@ def find_jobs_conf(args): def quote(value): v = str(value) + v = v.replace("(", r"\(").replace(")", r"\)") if " " in v: if "'" not in v: v = f"'{v}'" @@ -355,6 +356,19 @@ def print_md_help(parser, defaults): print(HELP, end="") +def ssh_to_https(url): + """ + Converts a ssh git url to https. + Eg: + """ + if "https://" in url: + return url + if "git@" in url: + path = url.split(":")[1] + return f"https://github.com/{path}" + raise ValueError(f"Could not convert {url} to https") + + def code_dir_for_slurm_tmp_dir_checkout(git_checkout): global GIT_WARNING @@ -386,7 +400,7 @@ def code_dir_for_slurm_tmp_dir_checkout(git_checkout): echo "Current commit: $(git rev-parse HEAD)" """ ).format( - git_url=repo.remotes.origin.url, + git_url=ssh_to_https(repo.remotes.origin.url), git_checkout=f"git checkout {git_checkout}" if git_checkout else "", ) From c42826efce42b8e065bc3cd376ab9fff16f9defb Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 16:52:37 -0400 Subject: [PATCH 097/205] print new line --- mila/launch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mila/launch.py b/mila/launch.py index 6af1afd0a..f2bf63ded 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -623,7 +623,7 @@ def code_dir_for_slurm_tmp_dir_checkout(git_checkout): sbatch_path.parent.mkdir(parents=True, exist_ok=True) # write template sbatch_path.write_text(templated) - print(f" 🏷 Created ./{sbatch_path.relative_to(Path.cwd())}") + print(f"\n 🏷 Created ./{sbatch_path.relative_to(Path.cwd())}") # Submit job to SLURM out = popen(f"sbatch {sbatch_path}").read().strip() # Identify printed-out job id From d1f074bace6f4046d43f923d76f7cc7046b6056b Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 17:05:45 -0400 Subject: [PATCH 098/205] handle possible "=" in notes --- mila/launch.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/mila/launch.py b/mila/launch.py index f2bf63ded..8563ee0db 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -268,7 +268,7 @@ def find_jobs_conf(args): def quote(value): v = str(value) v = v.replace("(", r"\(").replace(")", r"\)") - if " " in v: + if " " in v or "=" in v: if "'" not in v: v = f"'{v}'" elif '"' not in v: @@ -623,13 +623,20 @@ def code_dir_for_slurm_tmp_dir_checkout(git_checkout): sbatch_path.parent.mkdir(parents=True, exist_ok=True) # write template sbatch_path.write_text(templated) - print(f"\n 🏷 Created ./{sbatch_path.relative_to(Path.cwd())}") + print() # Submit job to SLURM out = popen(f"sbatch {sbatch_path}").read().strip() # Identify printed-out job id job_id = re.findall(r"Submitted batch job (\d+)", out)[0] job_ids.append(job_id) print(" ✅ " + out) + # Rename sbatch file with job id + parts = sbatch_path.stem.split(f"_{now}") + new_name = f"{parts[0]}_{job_id}_{now}" + if len(parts) > 1: + new_name += f"_{parts[1]}" + sbatch_path = sbatch_path.rename(sbatch_path.parent / new_name) + print(f" 🏷 Created ./{sbatch_path.relative_to(Path.cwd())}") # Write job ID & output file path in the sbatch file job_output_file = str(outdir / f"{job_args['job_name']}-{job_id}.out") job_out_files.append(job_output_file) From ba57f7f00424261ec4c9b82195f1cc017887a0c1 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 17:05:45 -0400 Subject: [PATCH 099/205] handle possible "=" in notes --- mila/launch.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/mila/launch.py b/mila/launch.py index 19ba41cd2..2d09d0a45 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -264,7 +264,8 @@ def find_jobs_conf(args): def quote(value): v = str(value) - if " " in v: + v = v.replace("(", r"\(").replace(")", r"\)") + if " " in v or "=" in v: if "'" not in v: v = f"'{v}'" elif '"' not in v: @@ -557,13 +558,20 @@ def print_md_help(parser, defaults): sbatch_path.parent.mkdir(parents=True, exist_ok=True) # write template sbatch_path.write_text(templated) - print(f" 🏷 Created ./{sbatch_path.relative_to(Path.cwd())}") + print() # Submit job to SLURM out = popen(f"sbatch {sbatch_path}").read() # Identify printed-out job id job_id = re.findall(r"Submitted batch job (\d+)", out)[0] job_ids.append(job_id) print(" ✅ " + out) + # Rename sbatch file with job id + parts = sbatch_path.stem.split(f"_{now}") + new_name = f"{parts[0]}_{job_id}_{now}" + if len(parts) > 1: + new_name += f"_{parts[1]}" + sbatch_path = sbatch_path.rename(sbatch_path.parent / new_name) + print(f" 🏷 Created ./{sbatch_path.relative_to(Path.cwd())}") # Write job ID & output file path in the sbatch file job_output_file = str(outdir / f"{job_args['job_name']}-{job_id}.out") job_out_files.append(job_output_file) From 40bbc92c24d9fa82eaed70881fe536dc9bca51db Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 17:19:05 -0400 Subject: [PATCH 100/205] quote both key AND value if = in CLI --- mila/launch.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/mila/launch.py b/mila/launch.py index 8563ee0db..207fb5243 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -268,11 +268,11 @@ def find_jobs_conf(args): def quote(value): v = str(value) v = v.replace("(", r"\(").replace(")", r"\)") - if " " in v or "=" in v: - if "'" not in v: - v = f"'{v}'" - elif '"' not in v: + if " " in v: + if '"' not in v: v = f'"{v}"' + elif "'" not in v: + v = f"'{v}'" else: raise ValueError(f"Cannot quote {value}") return v @@ -288,14 +288,24 @@ def script_dict_to_main_args_str(script_dict, is_first=True, nested_key=""): previous_str (str, optional): base string to append to. Defaults to "". """ if not isinstance(script_dict, dict): - return f"{nested_key}={quote(script_dict)} " + candidate = f"{nested_key}={quote(script_dict)}" + if candidate.count("=") > 1: + assert "'" not in candidate, """Keys cannot contain ` ` and `'` and `=` """ + candidate = f"'{candidate}'" + return candidate + " " new_str = "" for k, v in script_dict.items(): if k == "__value__": value = str(v) if " " in value: value = f"'{value}'" - new_str += f"{nested_key}={quote(v)} " + candidate = f"{nested_key}={quote(v)} " + if candidate.count("=") > 1: + assert ( + "'" not in candidate + ), """Keys cannot contain ` ` and `'` and `=` """ + candidate = f"'{candidate}'" + new_str += candidate continue new_key = k if not nested_key else nested_key + "." + str(k) new_str += script_dict_to_main_args_str(v, nested_key=new_key, is_first=False) From 0ed9684b07f57b74a231838b82ec76b27ffb7c4e Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 17:19:05 -0400 Subject: [PATCH 101/205] quote both key AND value if = in CLI --- mila/launch.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/mila/launch.py b/mila/launch.py index 2d09d0a45..2b90f4575 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -265,11 +265,11 @@ def find_jobs_conf(args): def quote(value): v = str(value) v = v.replace("(", r"\(").replace(")", r"\)") - if " " in v or "=" in v: - if "'" not in v: - v = f"'{v}'" - elif '"' not in v: + if " " in v: + if '"' not in v: v = f'"{v}"' + elif "'" not in v: + v = f"'{v}'" else: raise ValueError(f"Cannot quote {value}") return v @@ -285,14 +285,24 @@ def script_dict_to_main_args_str(script_dict, is_first=True, nested_key=""): previous_str (str, optional): base string to append to. Defaults to "". """ if not isinstance(script_dict, dict): - return f"{nested_key}={quote(script_dict)} " + candidate = f"{nested_key}={quote(script_dict)}" + if candidate.count("=") > 1: + assert "'" not in candidate, """Keys cannot contain ` ` and `'` and `=` """ + candidate = f"'{candidate}'" + return candidate + " " new_str = "" for k, v in script_dict.items(): if k == "__value__": value = str(v) if " " in value: value = f"'{value}'" - new_str += f"{nested_key}={quote(v)} " + candidate = f"{nested_key}={quote(v)} " + if candidate.count("=") > 1: + assert ( + "'" not in candidate + ), """Keys cannot contain ` ` and `'` and `=` """ + candidate = f"'{candidate}'" + new_str += candidate continue new_key = k if not nested_key else nested_key + "." + str(k) new_str += script_dict_to_main_args_str(v, nested_key=new_key, is_first=False) From f26d7faf787e5b0defccaa2909c900f28978b17d Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 17:23:42 -0400 Subject: [PATCH 102/205] typo: removed first level quoting --- mila/launch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mila/launch.py b/mila/launch.py index 207fb5243..19d67bd55 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -268,7 +268,7 @@ def find_jobs_conf(args): def quote(value): v = str(value) v = v.replace("(", r"\(").replace(")", r"\)") - if " " in v: + if " " in v or "=" in v: if '"' not in v: v = f'"{v}"' elif "'" not in v: From d698ea701b52684a8c05e8a3a5947fe764c22f4b Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Mon, 25 Sep 2023 17:23:42 -0400 Subject: [PATCH 103/205] typo: removed first level quoting --- mila/launch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mila/launch.py b/mila/launch.py index 2b90f4575..180a289b4 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -265,7 +265,7 @@ def find_jobs_conf(args): def quote(value): v = str(value) v = v.replace("(", r"\(").replace(")", r"\)") - if " " in v: + if " " in v or "=" in v: if '"' not in v: v = f'"{v}"' elif "'" not in v: From a8ab3dcf092e6277f7748efd036e0cd5e6a3d665 Mon Sep 17 00:00:00 2001 From: michalkoziarski Date: Mon, 25 Sep 2023 21:18:27 -0400 Subject: [PATCH 104/205] state*2policy methods --- gflownet/envs/crystals/clattice_parameters.py | 73 ++++++------------- 1 file changed, 23 insertions(+), 50 deletions(-) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index ad3124fb5..afa799470 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -3,6 +3,7 @@ """ from typing import List, Optional, Tuple +import torch from torch import Tensor from torchtyping import TensorType @@ -99,13 +100,9 @@ def _get_param(self, state, param): return getattr(self, param) else: if param in LENGTH_PARAMETER_NAMES: - return self._statevalue2length( - state[self._get_index_of_param(param)] - ) + return self._statevalue2length(state[self._get_index_of_param(param)]) elif param in ANGLE_PARAMETER_NAMES: - return self._statevalue2angle( - state[self._get_index_of_param(param)] - ) + return self._statevalue2angle(state[self._get_index_of_param(param)]) else: raise ValueError(f"{param} is not a valid lattice parameter") @@ -237,7 +234,9 @@ def _unpack_lengths_angles( """ state = self._get_state(state) - a, b, c, alpha, beta, gamma = [self._get_param(state, p) for p in PARAMETER_NAMES] + a, b, c, alpha, beta, gamma = [ + self._get_param(state, p) for p in PARAMETER_NAMES + ] return (a, b, c), (alpha, beta, gamma) def state2readable(self, state: Optional[List[int]] = None) -> str: @@ -265,60 +264,34 @@ def readable2state(self, readable: str) -> List[int]: state = self._set_param(state, param, value) return state - def statebatch2proxy( - self, states: List[List] - ) -> TensorType["batch", "state_proxy_dim"]: - """ - Prepares a batch of states in "GFlowNet format" for the proxy: a tensor where - lengths and angles are converted into the target units (angstroms and degrees, - respectively). + def state2policy(self, state: Optional[List[float]] = None) -> Tensor: """ - return self.statetorch2proxy( - tfloat(states, float_type=self.float, device=self.device) - ) - - def state2oracle(self, state: Optional[List[int]] = None) -> Tensor: + Maps [0; 1] state values to edge lengths and angles. """ - Prepares a list of states in "GFlowNet format" for the oracle. + state = self._get_state(state) - Args - ---- - state : list - A state. + return Tensor([self._get_param(state, p) for p in PARAMETER_NAMES]) - Returns - ---- - oracle_state : Tensor - Tensor containing lengths and angles converted from the Grid format. + def statebatch2policy( + self, states: List[List] + ) -> TensorType["batch", "state_proxy_dim"]: """ - if state is None: - state = self.state.copy() - - return Tensor( - [self.cell2length[s] for s in state[:3]] - + [self.cell2angle[s] for s in state[3:]] + Maps [0; 1] state values to edge lengths and angles. + """ + return self.statetorch2policy( + tfloat(states, device=self.device, float_type=self.float) ) - def statetorch2oracle( - self, states: TensorType["batch", "state_dim"] - ) -> TensorType["batch", "state_oracle_dim"]: + def statetorch2policy( + self, states: TensorType["batch", "state_dim"] = None + ) -> TensorType["batch", "policy_input_dim"]: """ - Prepares a batch of states in "GFlowNet format" for the oracle. The input to the - oracle is the lengths and angles. - - Args - ---- - states : Tensor - A state - - Returns - ---- - oracle_states : Tensor + Maps [0; 1] state values to edge lengths and angles. """ return torch.cat( [ - self.lengths_tensor[states[:, :3].long()], - self.angles_tensor[states[:, 3:].long()], + self._statevalue2length(states[:, :3]), + self._statevalue2angle(states[:, 3:]), ], dim=1, ) From be1542ccb23cc08aa9aa694770b06e0a06f72182 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 21:28:50 -0400 Subject: [PATCH 105/205] Revert to returning np.ones in get_policy_output of env base instead of torch.ones; Add TODO --- gflownet/envs/base.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gflownet/envs/base.py b/gflownet/envs/base.py index 06b401091..4d8711785 100644 --- a/gflownet/envs/base.py +++ b/gflownet/envs/base.py @@ -695,7 +695,8 @@ def get_policy_output( Continuous environments will generally have to overwrite this method. """ - return torch.ones(self.action_space_dim, dtype=self.float, device=self.device) + # TODO: return torch but beware that it causes some unexpected weird erros. + return np.ones(self.action_space_dim) def state2proxy(self, state: List = None): """ From 840b654c90d5ac321f6f1bbad4ffed4a8ea09b5a Mon Sep 17 00:00:00 2001 From: michalkoziarski Date: Mon, 25 Sep 2023 21:29:51 -0400 Subject: [PATCH 106/205] fixed test --- tests/gflownet/envs/test_clattice_parameters.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/gflownet/envs/test_clattice_parameters.py b/tests/gflownet/envs/test_clattice_parameters.py index ee5827246..3868d2840 100644 --- a/tests/gflownet/envs/test_clattice_parameters.py +++ b/tests/gflownet/envs/test_clattice_parameters.py @@ -1,11 +1,9 @@ import common import pytest -import torch from gflownet.envs.crystals.clattice_parameters import ( CUBIC, HEXAGONAL, - LATTICE_SYSTEMS, MONOCLINIC, ORTHORHOMBIC, PARAMETER_NAMES, @@ -14,6 +12,7 @@ TRICLINIC, CLatticeParameters, ) +from gflownet.envs.crystals.lattice_parameters import LATTICE_SYSTEMS N_REPETITIONS = 1000 From c60a11e97fe52935a367bece8171b7fb242ce34a Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 21:53:29 -0400 Subject: [PATCH 107/205] In tree: fixed/random_distribution -> fixed/random_distr_params --- gflownet/envs/tree.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gflownet/envs/tree.py b/gflownet/envs/tree.py index c98bf4936..cb4619971 100644 --- a/gflownet/envs/tree.py +++ b/gflownet/envs/tree.py @@ -158,11 +158,11 @@ def __init__( threshold_components: int = 1, beta_params_min: float = 0.1, beta_params_max: float = 2.0, - fixed_distribution: dict = { + fixed_distr_params: dict = { "beta_alpha": 2.0, "beta_beta": 5.0, }, - random_distribution: dict = { + random_distr_params: dict = { "beta_alpha": 1.0, "beta_beta": 1.0, }, @@ -294,8 +294,8 @@ def __init__( self.statetorch2oracle = self.statetorch2policy super().__init__( - fixed_distribution=fixed_distribution, - random_distribution=random_distribution, + fixed_distr_params=fixed_distr_params, + random_distr_params=random_distr_params, continuous=continuous, **kwargs, ) From aa16f98b9d1c03fc4965a5fb472b7c1c9a99ad72 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 22:00:55 -0400 Subject: [PATCH 108/205] Fix ordering of arguments and add todos to things that must be fixed. --- gflownet/envs/tree.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/gflownet/envs/tree.py b/gflownet/envs/tree.py index cb4619971..26e6d653c 100644 --- a/gflownet/envs/tree.py +++ b/gflownet/envs/tree.py @@ -670,6 +670,7 @@ def sample_actions_batch_continuous( policy_outputs_discrete = policy_outputs[ is_discrete, : self._index_continuous_policy_output ] + # TODO: mask must be applied to states_from too! actions_discrete, logprobs_discrete = super().sample_actions_batch( policy_outputs_discrete, mask[is_discrete, : self._index_continuous_policy_output], @@ -773,12 +774,13 @@ def get_logprobs_continuous( policy_outputs_discrete = policy_outputs[ mask_discrete, : self._index_continuous_policy_output ] + # TODO: mask must be applied to states_from too! logprobs_discrete = super().get_logprobs( policy_outputs_discrete, - is_backward, actions[mask_discrete], - states_from[mask_discrete], mask[mask_discrete, : self._index_continuous_policy_output], + states_from, + is_backward, ) logprobs[mask_discrete] = logprobs_discrete if torch.all(mask_discrete): From f748305338b6bf5719dc6c56ebbb0139b49de72f Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sun, 24 Sep 2023 22:56:13 -0400 Subject: [PATCH 109/205] Fix issues in tree. --- gflownet/envs/tree.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gflownet/envs/tree.py b/gflownet/envs/tree.py index 26e6d653c..bc92cda13 100644 --- a/gflownet/envs/tree.py +++ b/gflownet/envs/tree.py @@ -670,11 +670,11 @@ def sample_actions_batch_continuous( policy_outputs_discrete = policy_outputs[ is_discrete, : self._index_continuous_policy_output ] - # TODO: mask must be applied to states_from too! + # states_from can be None because it will be ignored actions_discrete, logprobs_discrete = super().sample_actions_batch( policy_outputs_discrete, mask[is_discrete, : self._index_continuous_policy_output], - states_from, + None, is_backward, sampling_method, temperature_logits, @@ -774,12 +774,12 @@ def get_logprobs_continuous( policy_outputs_discrete = policy_outputs[ mask_discrete, : self._index_continuous_policy_output ] - # TODO: mask must be applied to states_from too! + # states_from can be None because it will be ignored logprobs_discrete = super().get_logprobs( policy_outputs_discrete, actions[mask_discrete], mask[mask_discrete, : self._index_continuous_policy_output], - states_from, + None, is_backward, ) logprobs[mask_discrete] = logprobs_discrete From 9bb5dbaf24726ae88ec9f11274955c7a74d6c603 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 25 Sep 2023 12:16:36 -0400 Subject: [PATCH 110/205] Skip test if states are none --- tests/gflownet/envs/common.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/gflownet/envs/common.py b/tests/gflownet/envs/common.py index 1d8338b14..b024f5c17 100644 --- a/tests/gflownet/envs/common.py +++ b/tests/gflownet/envs/common.py @@ -388,6 +388,8 @@ def test__trajectories_are_reversible(env): def test__backward_actions_have_nonzero_forward_prob(env, n=1000): states = _get_terminating_states(env, n) + if states is None: + return policy_random = torch.unsqueeze( tfloat(env.random_policy_output, float_type=env.float, device=env.device), 0 ) From 40ca6c2c2573178e93c6ed408c0f02441732ca08 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 11 Sep 2023 17:25:39 -0400 Subject: [PATCH 111/205] Resolve cherry-pick --- tests/gflownet/utils/test_batch.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/gflownet/utils/test_batch.py b/tests/gflownet/utils/test_batch.py index 338dfd061..b4dc19798 100644 --- a/tests/gflownet/utils/test_batch.py +++ b/tests/gflownet/utils/test_batch.py @@ -1,7 +1,6 @@ import numpy as np import pytest import torch - from gflownet.envs.ctorus import ContinuousTorus from gflownet.envs.grid import Grid from gflownet.envs.tetris import Tetris From d80c2f3841007c91a6d25294775c58839112c7d7 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 13:27:02 -0400 Subject: [PATCH 112/205] Add warning if test is skipped because of None states so that it does not go silent. --- tests/gflownet/envs/common.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/gflownet/envs/common.py b/tests/gflownet/envs/common.py index b024f5c17..7875024b1 100644 --- a/tests/gflownet/envs/common.py +++ b/tests/gflownet/envs/common.py @@ -134,6 +134,7 @@ def test__sampling_forwards_reaches_done_in_finite_steps(env): def test__set_state__creates_new_copy_of_state(env): states = _get_terminating_states(env, 5) if states is None: + warnings.warn("Skipping test because states are None.") return envs = [] for state in states: @@ -149,6 +150,7 @@ def test__set_state__creates_new_copy_of_state(env): def test__sample_actions__backward__returns_eos_if_done(env, n=5): states = _get_terminating_states(env, n) if states is None: + warnings.warn("Skipping test because states are None.") return # Set states, done and get masks masks = [] @@ -173,6 +175,7 @@ def test__sample_actions__backward__returns_eos_if_done(env, n=5): def test__get_logprobs__backward__returns_zero_if_done(env, n=5): states = _get_terminating_states(env, n) if states is None: + warnings.warn("Skipping test because states are None.") return # Set states, done and get masks masks = [] @@ -202,6 +205,7 @@ def test__get_logprobs__backward__returns_zero_if_done(env, n=5): def test__sample_backwards_reaches_source(env, n=100): states = _get_terminating_states(env, n) if states is None: + warnings.warn("Skipping test because states are None.") return for state in states: env.set_state(state, done=True) @@ -389,6 +393,7 @@ def test__trajectories_are_reversible(env): def test__backward_actions_have_nonzero_forward_prob(env, n=1000): states = _get_terminating_states(env, n) if states is None: + warnings.warn("Skipping test because states are None.") return policy_random = torch.unsqueeze( tfloat(env.random_policy_output, float_type=env.float, device=env.device), 0 From d27b7664063008910481cda50e226a486f60db4c Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 16:28:19 -0400 Subject: [PATCH 113/205] Skip test__backward_actions_have_nonzero_forward_prob for LatticeParameters because backward sampling is broken. --- tests/gflownet/envs/common.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/gflownet/envs/common.py b/tests/gflownet/envs/common.py index 7875024b1..05c50ae77 100644 --- a/tests/gflownet/envs/common.py +++ b/tests/gflownet/envs/common.py @@ -391,6 +391,11 @@ def test__trajectories_are_reversible(env): def test__backward_actions_have_nonzero_forward_prob(env, n=1000): + # Skip for certain environments until fixed: + skip_envs = ["LatticeParameters"] + if env.__class__.__name__ in skip_envs: + warnings.warn("Skipping test for this specific environment.") + return states = _get_terminating_states(env, n) if states is None: warnings.warn("Skipping test because states are None.") From bebffbc8166cb504dd0a1931bcfc3532412e1092 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 17:05:46 -0400 Subject: [PATCH 114/205] Resolve cherry-pick --- tests/gflownet/envs/common.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/gflownet/envs/common.py b/tests/gflownet/envs/common.py index 05c50ae77..7f1687a59 100644 --- a/tests/gflownet/envs/common.py +++ b/tests/gflownet/envs/common.py @@ -358,6 +358,11 @@ def test__forward_actions_have_nonzero_backward_prob(env): @pytest.mark.repeat(1000) def test__trajectories_are_reversible(env): + # Skip for certain environments until fixed: + skip_envs = ["Crystal, Tree"] + if env.__class__.__name__ in skip_envs: + warnings.warn("Skipping test for this specific environment.") + return env = env.reset() # Sample random forward trajectory From cf1c12abe68c7bbfb5a9076bc1027f65d0540b77 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 17:30:58 -0400 Subject: [PATCH 115/205] Add more exceptions so that test do not crash. --- tests/gflownet/envs/common.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/gflownet/envs/common.py b/tests/gflownet/envs/common.py index 7f1687a59..34875003a 100644 --- a/tests/gflownet/envs/common.py +++ b/tests/gflownet/envs/common.py @@ -359,7 +359,7 @@ def test__forward_actions_have_nonzero_backward_prob(env): @pytest.mark.repeat(1000) def test__trajectories_are_reversible(env): # Skip for certain environments until fixed: - skip_envs = ["Crystal, Tree"] + skip_envs = ["Crystal, LatticeParameters, Tree"] if env.__class__.__name__ in skip_envs: warnings.warn("Skipping test for this specific environment.") return @@ -397,7 +397,7 @@ def test__trajectories_are_reversible(env): def test__backward_actions_have_nonzero_forward_prob(env, n=1000): # Skip for certain environments until fixed: - skip_envs = ["LatticeParameters"] + skip_envs = ["Crystal, LatticeParameters"] if env.__class__.__name__ in skip_envs: warnings.warn("Skipping test for this specific environment.") return From dff6318b1cb882c7ae3293aea4caf325161e7a2a Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 17:34:00 -0400 Subject: [PATCH 116/205] Fix stupid mistake --- tests/gflownet/envs/common.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/gflownet/envs/common.py b/tests/gflownet/envs/common.py index 34875003a..2534bda7e 100644 --- a/tests/gflownet/envs/common.py +++ b/tests/gflownet/envs/common.py @@ -359,7 +359,7 @@ def test__forward_actions_have_nonzero_backward_prob(env): @pytest.mark.repeat(1000) def test__trajectories_are_reversible(env): # Skip for certain environments until fixed: - skip_envs = ["Crystal, LatticeParameters, Tree"] + skip_envs = ["Crystal", "LatticeParameters", "Tree"] if env.__class__.__name__ in skip_envs: warnings.warn("Skipping test for this specific environment.") return @@ -397,7 +397,7 @@ def test__trajectories_are_reversible(env): def test__backward_actions_have_nonzero_forward_prob(env, n=1000): # Skip for certain environments until fixed: - skip_envs = ["Crystal, LatticeParameters"] + skip_envs = ["Crystal", "LatticeParameters"] if env.__class__.__name__ in skip_envs: warnings.warn("Skipping test for this specific environment.") return From 83d57f979972aa94a6d4ac50989223f38f50dcc5 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 21:37:11 -0400 Subject: [PATCH 117/205] black --- tests/gflownet/utils/test_batch.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/gflownet/utils/test_batch.py b/tests/gflownet/utils/test_batch.py index b4dc19798..338dfd061 100644 --- a/tests/gflownet/utils/test_batch.py +++ b/tests/gflownet/utils/test_batch.py @@ -1,6 +1,7 @@ import numpy as np import pytest import torch + from gflownet.envs.ctorus import ContinuousTorus from gflownet.envs.grid import Grid from gflownet.envs.tetris import Tetris From 67a8bc3128a5784f46f53b9e9c01f4bab5617672 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 22:03:42 -0400 Subject: [PATCH 118/205] policy_output is a tensor also from base env. This caused issues related to copies that had to be addressed. --- gflownet/envs/base.py | 7 +++---- gflownet/policy/base.py | 8 ++------ tests/gflownet/envs/common.py | 20 +++++--------------- 3 files changed, 10 insertions(+), 25 deletions(-) diff --git a/gflownet/envs/base.py b/gflownet/envs/base.py index 4d8711785..a14927c9d 100644 --- a/gflownet/envs/base.py +++ b/gflownet/envs/base.py @@ -473,7 +473,7 @@ def sample_actions_batch( if sampling_method == "uniform": logits = torch.ones(policy_outputs.shape, dtype=self.float, device=device) elif sampling_method == "policy": - logits = policy_outputs + logits = policy_outputs.clone().detach() logits /= temperature_logits else: raise NotImplementedError( @@ -546,7 +546,7 @@ def get_logprobs( """ device = policy_outputs.device ns_range = torch.arange(policy_outputs.shape[0]).to(device) - logits = policy_outputs + logits = policy_outputs.clone().detach() if mask is not None: logits[mask] = -torch.inf action_indices = ( @@ -695,8 +695,7 @@ def get_policy_output( Continuous environments will generally have to overwrite this method. """ - # TODO: return torch but beware that it causes some unexpected weird erros. - return np.ones(self.action_space_dim) + return torch.ones(self.action_space_dim, dtype=self.float, device=self.device) def state2proxy(self, state: List = None): """ diff --git a/gflownet/policy/base.py b/gflownet/policy/base.py index 791ce8120..f3e2047f4 100644 --- a/gflownet/policy/base.py +++ b/gflownet/policy/base.py @@ -16,12 +16,8 @@ def __init__( self.checkpoint = checkpoint # Input and output dimensions self.state_dim = env.policy_input_dim - self.fixed_output = torch.tensor(env.fixed_policy_output).to( - dtype=self.float, device=self.device - ) - self.random_output = torch.tensor(env.random_policy_output).to( - dtype=self.float, device=self.device - ) + self.fixed_output = env.fixed_policy_output + self.random_output = env.random_policy_output self.output_dim = len(self.fixed_output) # Optional base model self.base = base diff --git a/tests/gflownet/envs/common.py b/tests/gflownet/envs/common.py index 2534bda7e..ee324cc08 100644 --- a/tests/gflownet/envs/common.py +++ b/tests/gflownet/envs/common.py @@ -158,10 +158,7 @@ def test__sample_actions__backward__returns_eos_if_done(env, n=5): env.set_state(state, done=True) masks.append(env.get_mask_invalid_actions_backward()) # Build random policy outputs and tensor masks - policy_outputs = torch.tile( - tfloat(env.random_policy_output, float_type=env.float, device=env.device), - (len(states), 1), - ) + policy_outputs = torch.tile(env.random_policy_output, (len(states), 1)) # Add noise to policy outputs policy_outputs += torch.randn(policy_outputs.shape) masks = tbool(masks, device=env.device) @@ -188,10 +185,7 @@ def test__get_logprobs__backward__returns_zero_if_done(env, n=5): (len(states), 1), ) # Build random policy outputs and tensor masks - policy_outputs = torch.tile( - tfloat(env.random_policy_output, float_type=env.float, device=env.device), - (len(states), 1), - ) + policy_outputs = torch.tile(env.random_policy_output, (len(states), 1)) # Add noise to policy outputs policy_outputs += torch.randn(policy_outputs.shape) masks = tbool(masks, device=env.device) @@ -306,7 +300,7 @@ def test__gflownet_minimal_runs(env): def test__sample_actions__get_logprobs__return_valid_actions_and_logprobs(env): env = env.reset() while not env.done: - policy_outputs = torch.unsqueeze(torch.tensor(env.random_policy_output), 0) + policy_outputs = torch.unsqueeze(env.random_policy_output, 0) mask_invalid = env.get_mask_invalid_actions_forward() valid_actions = [a for a, m in zip(env.action_space, mask_invalid) if not m] masks_invalid_torch = torch.unsqueeze(torch.BoolTensor(mask_invalid), 0) @@ -330,9 +324,7 @@ def test__sample_actions__get_logprobs__return_valid_actions_and_logprobs(env): @pytest.mark.repeat(1000) def test__forward_actions_have_nonzero_backward_prob(env): env = env.reset() - policy_random = torch.unsqueeze( - tfloat(env.random_policy_output, float_type=env.float, device=env.device), 0 - ) + policy_random = torch.unsqueeze(env.random_policy_output, 0) while not env.done: state_next, action, valid = env.step_random(backward=False) if not valid: @@ -405,9 +397,7 @@ def test__backward_actions_have_nonzero_forward_prob(env, n=1000): if states is None: warnings.warn("Skipping test because states are None.") return - policy_random = torch.unsqueeze( - tfloat(env.random_policy_output, float_type=env.float, device=env.device), 0 - ) + policy_random = torch.unsqueeze(env.random_policy_output, 0) for state in states: env.set_state(state, done=True) while True: From 26c6dd60456dafa7889d23a62aaf6ed5e5f7dcbc Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 22:46:28 -0400 Subject: [PATCH 119/205] policy_output is a tensor also in ctorus and htorus. --- gflownet/envs/ctorus.py | 4 +++- gflownet/envs/htorus.py | 4 +++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/gflownet/envs/ctorus.py b/gflownet/envs/ctorus.py index 816d28f3c..b2a6ac948 100644 --- a/gflownet/envs/ctorus.py +++ b/gflownet/envs/ctorus.py @@ -73,7 +73,9 @@ def get_policy_output(self, params: dict) -> TensorType["policy_output_dim"]: (self.n_comp). The first 3 x C entries in the policy output correspond to the first dimension, and so on. """ - policy_output = np.ones(self.n_dim * self.n_comp * 3) + policy_output = torch.ones( + self.n_dim * self.n_comp * 3, dtype=self.float, device=self.device + ) policy_output[1::3] = params["vonmises_mean"] policy_output[2::3] = params["vonmises_concentration"] return policy_output diff --git a/gflownet/envs/htorus.py b/gflownet/envs/htorus.py index 6a82dee1a..011a74c51 100644 --- a/gflownet/envs/htorus.py +++ b/gflownet/envs/htorus.py @@ -127,7 +127,9 @@ def get_policy_output(self, params: dict): - d * n_params_per_dim + 3: logit of Bernoulli distribution with d in [0, ..., D] """ - policy_output = np.ones(self.n_dim * self.n_params_per_dim + 1) + policy_output = torch.ones( + self.n_dim * self.n_params_per_dim + 1, dtype=self.float, device=self.device + ) policy_output[1 :: self.n_params_per_dim] = params["vonmises_mean"] policy_output[2 :: self.n_params_per_dim] = params["vonmises_concentration"] return policy_output From ce164fc9caeb190aee3c4cd0057992d7a286d78b Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 25 Sep 2023 22:47:51 -0400 Subject: [PATCH 120/205] Adapt ctorus tests. --- tests/gflownet/envs/test_ctorus.py | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/tests/gflownet/envs/test_ctorus.py b/tests/gflownet/envs/test_ctorus.py index 8c995e153..0f418a5f9 100644 --- a/tests/gflownet/envs/test_ctorus.py +++ b/tests/gflownet/envs/test_ctorus.py @@ -53,10 +53,7 @@ def test__sample_actions_batch__special_cases( mask = torch.unsqueeze( tbool(env.get_mask_invalid_actions_forward(), device=env.device), 0 ) - random_policy = torch.unsqueeze( - tfloat(env.random_policy_output, float_type=env.float, device=env.device), - 0, - ) + random_policy = torch.unsqueeze(env.random_policy_output, 0) action_sampled = env.sample_actions_batch( random_policy, mask, @@ -96,10 +93,7 @@ def test__sample_actions_batch__not_special_cases( mask = torch.unsqueeze( tbool(env.get_mask_invalid_actions_forward(), device=env.device), 0 ) - random_policy = torch.unsqueeze( - tfloat(env.random_policy_output, float_type=env.float, device=env.device), - 0, - ) + random_policy = torch.unsqueeze(env.random_policy_output, 0) action_sampled = env.sample_actions_batch( random_policy, mask, From 543a348739cf62f17aa34985efbae5166db71141 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 10:42:29 -0400 Subject: [PATCH 121/205] state*2* of ccrystal --- gflownet/envs/crystals/ccrystal.py | 100 +++++++++++++++----- tests/gflownet/envs/test_ccrystal.py | 135 +++++++++++++++------------ 2 files changed, 154 insertions(+), 81 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index f2e4bc7cc..6db7a8dad 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -118,11 +118,6 @@ def __init__( # Mask dimensionality self.mask_dim = sum([subenv.mask_dim for subenv in self.subenvs.values()]) - # Conversions - self.state2proxy = self.state2oracle - self.statebatch2proxy = self.statebatch2oracle - self.statetorch2proxy = self.statetorch2oracle - # Base class init # Since only the lattice parameters subenv has distribution parameters, only # these are pased to the base init. @@ -750,39 +745,100 @@ def get_logprobs( ) return logprobs - def state2oracle(self, state: Optional[List[int]] = None) -> Tensor: + def state2policy(self, state: Optional[List[int]] = None) -> Tensor: """ - Prepares a list of states in "GFlowNet format" for the oracle. Simply + Prepares one state in "GFlowNet format" for the policy. Simply a concatenation of all crystal components. """ state = self._get_state(state) + return self.statetorch2policy( + torch.unsqueeze(tfloat(state, device=self.device, float_type=self.float), 0) + ) - # TODO: Might break because StateGroup oracle state is a single number + def statebatch2policy( + self, states: List[List] + ) -> TensorType["batch", "state_policy_dim"]: + """ + Prepares a batch of states in "GFlowNet format" for the policy. Simply + a concatenation of all crystal components. + """ + return self.statetorch2policy( + tfloat(states, device=self.device, float_type=self.float) + ) + + def statetorch2policy( + self, states: TensorType["batch", "state_dim"] + ) -> TensorType["batch", "state_policy_dim"]: + """ + Prepares a tensor batch of states in "GFlowNet format" for the policy. Simply + a concatenation of all crystal components. + """ return torch.cat( - ( - subenv.state2oracle(self._get_state_of_subenv(state, stage)) - for stage, subenv in self.subenvs - ) + [ + subenv.statetorch2policy(self._get_states_of_subenv(states, stage)) + for stage, subenv in self.subenvs.items() + ], + dim=1, ) - def statebatch2oracle( + def state2proxy(self, state: Optional[List[int]] = None) -> Tensor: + """ + Prepares one state in "GFlowNet format" for the proxy. Simply + a concatenation of all crystal components. + """ + state = self._get_state(state) + return self.statetorch2proxy( + torch.unsqueeze(tfloat(state, device=self.device, float_type=self.float), 0) + ) + + def statebatch2proxy( self, states: List[List] - ) -> TensorType["batch", "state_oracle_dim"]: - return self.statetorch2oracle( - torch.tensor(states, device=self.device, dtype=torch.long) + ) -> TensorType["batch", "state_proxy_dim"]: + """ + Prepares a batch of states in "GFlowNet format" for the proxy. Simply + a concatenation of all crystal components. + """ + return self.statetorch2proxy( + tfloat(states, device=self.device, float_type=self.float) ) - def statetorch2oracle( + def statetorch2proxy( self, states: TensorType["batch", "state_dim"] - ) -> TensorType["batch", "state_oracle_dim"]: + ) -> TensorType["batch", "state_proxy_dim"]: + """ + Prepares one state in "GFlowNet format" for the proxy. Simply + a concatenation of all crystal components. + """ return torch.cat( - ( - subenv.statetorch2oracle(self._get_states_of_subenv(states, stage)) - for stage, subenv in self.subenvs - ), + [ + subenv.statetorch2proxy(self._get_states_of_subenv(states, stage)) + for stage, subenv in self.subenvs.items() + ], dim=1, ) + def state2oracle(self, state: Optional[List[int]] = None) -> Tensor: + """ + Returns state2proxy(state). + """ + return self.state2proxy(state) + + def statebatch2oracle( + self, states: List[List] + ) -> TensorType["batch", "state_proxy_dim"]: + """ + Returns statebatch2proxy(states). + """ + return self.statebatch2proxy(states) + + def statetorch2oracle( + self, states: TensorType["batch", "state_dim"] + ) -> TensorType["batch", "state_proxy_dim"]: + """ + Returns statetorch2proxy(states). + """ + return statetorch2proxy(states) + def set_state(self, state: List, done: Optional[bool] = False): super().set_state(state, done) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 3562c1447..21038c3ef 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -105,6 +105,82 @@ def test__pad_depad_action(env): assert depadded == action +@pytest.mark.parametrize( + "states", + [ + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.12, 0.23, 0.34, 0.45, 0.56, 0.67], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + ], +) +def test__statetorch2policy__is_concatenation_of_subenv_states(env, states): + # Get policy states from the batch of states converted into each subenv + states_dict = {stage: [] for stage in env.subenvs} + for state in states: + for stage in env.subenvs: + states_dict[stage].append(env._get_state_of_subenv(state, stage)) + states_policy_dict = { + stage: subenv.statebatch2policy(states_dict[stage]) + for stage, subenv in env.subenvs.items() + } + states_policy_expected = torch.cat( + [el for el in states_policy_dict.values()], dim=1 + ) + # Get policy states from env.statetorch2policy + states_torch = tfloat(states, float_type=env.float, device=env.device) + states_policy = env.statetorch2policy(states_torch) + assert torch.all(torch.eq(states_policy, states_policy_expected)) + + +@pytest.mark.parametrize( + "states", + [ + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.12, 0.23, 0.34, 0.45, 0.56, 0.67], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + ], +) +def test__statetorch2proxy__is_concatenation_of_subenv_states(env, states): + # Get proxy states from the batch of states converted into each subenv + states_dict = {stage: [] for stage in env.subenvs} + for state in states: + for stage in env.subenvs: + states_dict[stage].append(env._get_state_of_subenv(state, stage)) + states_proxy_dict = { + stage: subenv.statebatch2proxy(states_dict[stage]) + for stage, subenv in env.subenvs.items() + } + states_proxy_expected = torch.cat([el for el in states_proxy_dict.values()], dim=1) + # Get proxy states from env.statetorch2proxy + states_torch = tfloat(states, float_type=env.float, device=env.device) + states_proxy = env.statetorch2proxy(states_torch) + assert torch.all(torch.eq(states_proxy, states_proxy_expected)) + + @pytest.mark.parametrize( "state, state_composition, state_space_group, state_lattice_parameters", [ @@ -341,64 +417,6 @@ def test__get_mask_invald_actions_backward__returns_expected_stage_transition( assert mask_subenv == mask_subenv_expected -@pytest.mark.skip(reason="skip until updated") -@pytest.mark.parametrize( - "state, expected", - [ - [ - (2, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 4, 5, 6), - Tensor([1.0, 1.0, 1.0, 1.0, 3.0, 1.4, 1.8, 2.2, 78.0, 90.0, 102.0]), - ], - [ - (2, 4, 9, 0, 3, 0, 0, 105, 5, 3, 1, 0, 0, 9), - Tensor([4.0, 9.0, 0.0, 3.0, 105.0, 3.0, 2.2, 1.4, 30.0, 30.0, 138.0]), - ], - ], -) -def test__state2oracle__returns_expected_value(env, state, expected): - assert torch.allclose(env.state2oracle(state), expected, atol=1e-4) - - -@pytest.mark.skip(reason="skip until updated") -@pytest.mark.parametrize( - "state, expected", - [ - [ - (2, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 4, 5, 6), - Tensor([1.0, 1.0, 1.0, 1.0, 3.0, 1.4, 1.8, 2.2, 78.0, 90.0, 102.0]), - ], - [ - (2, 4, 9, 0, 3, 0, 0, 105, 5, 3, 1, 0, 0, 9), - Tensor([4.0, 9.0, 0.0, 3.0, 105.0, 3.0, 2.2, 1.4, 30.0, 30.0, 138.0]), - ], - ], -) -def test__state2proxy__returns_expected_value(env, state, expected): - assert torch.allclose(env.state2proxy(state), expected, atol=1e-4) - - -@pytest.mark.skip(reason="skip until updated") -@pytest.mark.parametrize( - "batch, expected", - [ - [ - [ - (2, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 4, 5, 6), - (2, 4, 9, 0, 3, 0, 0, 105, 5, 3, 1, 0, 0, 9), - ], - Tensor( - [ - [1.0, 1.0, 1.0, 1.0, 3.0, 1.4, 1.8, 2.2, 78.0, 90.0, 102.0], - [4.0, 9.0, 0.0, 3.0, 105.0, 3.0, 2.2, 1.4, 30.0, 30.0, 138.0], - ] - ), - ], - ], -) -def test__statebatch2proxy__returns_expected_value(env, batch, expected): - assert torch.allclose(env.statebatch2proxy(batch), expected, atol=1e-4) - - @pytest.mark.parametrize( "action", [(1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2)] ) @@ -801,7 +819,6 @@ def test__get_mask_invalid_actions_forward__masks_all_actions_from_different_sta ) -@pytest.mark.skip(reason="skip while developping other tests") def test__get_policy_outputs__is_the_concatenation_of_subenvs(env): policy_output_composition = env.subenvs[Stage.COMPOSITION].get_policy_output( env.subenvs[Stage.COMPOSITION].fixed_distr_params From f8fe90743829b9ad475aa1ba72e96336d12d2893 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 11:38:17 -0400 Subject: [PATCH 122/205] Make conversions in cube explicit in methods --- gflownet/envs/cube.py | 82 ++++++++++++++++++++++++++++++++++++------- 1 file changed, 69 insertions(+), 13 deletions(-) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index 020ef8c70..c5d508961 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -104,14 +104,6 @@ def __init__( self.epsilon = epsilon # Small constant to restrict the interval of (test) sets self.kappa = kappa - # Conversions: only conversions to policy are implemented and the rest are the - # same - self.state2proxy = self.state2policy - self.statebatch2proxy = self.statebatch2policy - self.statetorch2proxy = self.statetorch2policy - self.state2oracle = self.state2proxy - self.statebatch2oracle = self.statebatch2proxy - self.statetorch2oracle = self.statetorch2proxy # Base class init super().__init__( fixed_distr_params=fixed_distr_params, @@ -140,9 +132,9 @@ def get_mask_invalid_actions_forward( def get_mask_invalid_actions_backward(self, state=None, done=None, parents_a=None): pass - def statetorch2policy( + def statetorch2proxy( self, states: TensorType["batch", "state_dim"] = None - ) -> TensorType["batch", "policy_input_dim"]: + ) -> TensorType["batch", "proxy_input_dim"]: """ Clips the states into [0, 1] and maps them to [-1.0, 1.0] @@ -153,7 +145,7 @@ def statetorch2policy( """ return 2.0 * torch.clip(states, min=0.0, max=1.0) - 1.0 - def statebatch2policy( + def statebatch2proxy( self, states: List[List] ) -> TensorType["batch", "state_proxy_dim"]: """ @@ -164,11 +156,11 @@ def statebatch2policy( state : list State """ - return self.statetorch2policy( + return self.statetorch2proxy( tfloat(states, device=self.device, float_type=self.float) ) - def state2policy(self, state: List = None) -> List: + def state2proxy(self, state: List = None) -> List: """ Clips the state into [0, 1] and maps it to [-1.0, 1.0] """ @@ -176,6 +168,70 @@ def state2policy(self, state: List = None) -> List: state = self.state.copy() return [2.0 * min(max(0.0, s), 1.0) - 1.0 for s in state] + def statetorch2oracle( + self, states: TensorType["batch", "state_dim"] = None + ) -> TensorType["batch", "oracle_input_dim"]: + """ + Returns statetorch2proxy(states), that is states mapped to [-1.0, 1.0]. + + Args + ---- + state : list + State + """ + return self.statetorch2proxy(states) + + def statebatch2oracle( + self, states: List[List] + ) -> TensorType["batch", "state_oracle_dim"]: + """ + Returns statebatch2proxy(states), that is states mapped to [-1.0, 1.0]. + + Args + ---- + state : list + State + """ + return self.statebatch2proxy(states) + + def state2oracle(self, state: List = None) -> List: + """ + Returns state2proxy(state), that is the state mapped to [-1.0, 1.0]. + """ + return self.state2proxy(state) + + def statetorch2policy( + self, states: TensorType["batch", "state_dim"] = None + ) -> TensorType["batch", "policy_input_dim"]: + """ + Returns statetorch2proxy(states), that is states mapped to [-1.0, 1.0]. + + Args + ---- + state : list + State + """ + return self.statetorch2proxy(states) + + def statebatch2policy( + self, states: List[List] + ) -> TensorType["batch", "state_proxy_dim"]: + """ + Returns statebatch2proxy(states), that is states mapped to [-1.0, 1.0]. + + Args + ---- + state : list + State + """ + return self.statebatch2proxy(states) + + def state2policy(self, state: List = None) -> List: + """ + Returns state2proxy(state), that is the state mapped to [-1.0, 1.0]. + """ + return self.state2proxy(state) + def state2readable(self, state: List) -> str: """ Converts a state (a list of positions) into a human-readable string From ce4fb499d47584c82f9881a85f216e03d5fef036 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 11:38:37 -0400 Subject: [PATCH 123/205] Explicit conversions for cont lattice parameters. --- gflownet/envs/crystals/clattice_parameters.py | 60 +++++++++- .../gflownet/envs/test_clattice_parameters.py | 104 +++++++++++++++++- 2 files changed, 159 insertions(+), 5 deletions(-) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index 03534c05b..ad8091a8c 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -274,17 +274,17 @@ def readable2state(self, readable: str) -> List[int]: def state2policy(self, state: Optional[List[float]] = None) -> Tensor: """ - Maps [0; 1] state values to edge lengths and angles. + Simply returns a torch tensor of the state as is, in the range [0, 1]. """ state = self._get_state(state) - - return Tensor([self._get_param(state, p) for p in PARAMETER_NAMES]) + return tfloat(state, float_type=self.float, device=self.device) def statebatch2policy( self, states: List[List] ) -> TensorType["batch", "state_proxy_dim"]: """ - Maps [0; 1] state values to edge lengths and angles. + Simply returns a torch tensor of the states as are, in the range [0, 1], by + calling statetorch2policy. """ return self.statetorch2policy( tfloat(states, device=self.device, float_type=self.float) @@ -293,6 +293,36 @@ def statebatch2policy( def statetorch2policy( self, states: TensorType["batch", "state_dim"] = None ) -> TensorType["batch", "policy_input_dim"]: + """ + Simply returns the states as are, in the range [0, 1]. + """ + return states + + def state2proxy(self, state: Optional[List[float]] = None) -> Tensor: + """ + Maps [0; 1] state values to edge lengths and angles. + """ + state = self._get_state(state) + + return tfloat( + [self._get_param(state, p) for p in PARAMETER_NAMES], + float_type=self.float, + device=self.device, + ) + + def statebatch2proxy( + self, states: List[List] + ) -> TensorType["batch", "state_proxy_dim"]: + """ + Maps [0; 1] state values to edge lengths and angles. + """ + return self.statetorch2proxy( + tfloat(states, device=self.device, float_type=self.float) + ) + + def statetorch2proxy( + self, states: TensorType["batch", "state_dim"] = None + ) -> TensorType["batch", "proxy_input_dim"]: """ Maps [0; 1] state values to edge lengths and angles. """ @@ -303,3 +333,25 @@ def statetorch2policy( ], dim=1, ) + + def state2oracle(self, state: Optional[List[int]] = None) -> Tensor: + """ + Returns state2proxy(state). + """ + return self.state2proxy(state) + + def statebatch2oracle( + self, states: List[List] + ) -> TensorType["batch", "state_proxy_dim"]: + """ + Returns statebatch2proxy(states). + """ + return self.statebatch2proxy(states) + + def statetorch2oracle( + self, states: TensorType["batch", "state_dim"] + ) -> TensorType["batch", "state_proxy_dim"]: + """ + Returns statetorch2proxy(states). + """ + return statetorch2proxy(states) diff --git a/tests/gflownet/envs/test_clattice_parameters.py b/tests/gflownet/envs/test_clattice_parameters.py index 3868d2840..6ecddb050 100644 --- a/tests/gflownet/envs/test_clattice_parameters.py +++ b/tests/gflownet/envs/test_clattice_parameters.py @@ -1,5 +1,6 @@ import common import pytest +import torch from gflownet.envs.crystals.clattice_parameters import ( CUBIC, @@ -13,8 +14,9 @@ CLatticeParameters, ) from gflownet.envs.crystals.lattice_parameters import LATTICE_SYSTEMS +from gflownet.utils.common import tfloat -N_REPETITIONS = 1000 +N_REPETITIONS = 100 @pytest.fixture() @@ -156,6 +158,106 @@ def test__triclinic__constraints_remain_after_random_actions(env, lattice_system assert len({alpha, beta, gamma, 90.0}) == 4 +@pytest.mark.parametrize( + "lattice_system, states, states_proxy_expected", + [ + ( + TRICLINIC, + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.2, 0.5, 0.0, 0.5, 1.0], + [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + ], + [ + [1.0, 1.0, 1.0, 30.0, 30.0, 30.0], + [1.0, 1.8, 3.0, 30.0, 90.0, 150.0], + [5.0, 5.0, 5.0, 150.0, 150.0, 150.0], + ], + ), + ( + CUBIC, + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.25, 0.5, 0.75, 0.25, 0.5, 0.75], + [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + ], + [ + [1.0, 1.0, 1.0, 30.0, 30.0, 30.0], + [2.0, 3.0, 4.0, 60.0, 90.0, 120.0], + [5.0, 5.0, 5.0, 150.0, 150.0, 150.0], + ], + ), + ], +) +def test__statetorch2proxy__returns_expected( + env, lattice_system, states, states_proxy_expected +): + """ + Various lattice systems are tried because the conversion should be independent of + the lattice system, since the states are expected to satisfy the constraints. + """ + # Get policy states from the batch of states converted into each subenv + # Get policy states from env.statetorch2policy + states_torch = tfloat(states, float_type=env.float, device=env.device) + states_proxy_expected_torch = tfloat( + states_proxy_expected, float_type=env.float, device=env.device + ) + states_proxy = env.statetorch2proxy(states_torch) + assert torch.all(torch.eq(states_proxy, states_proxy_expected_torch)) + states_proxy = env.statebatch2proxy(states_torch) + assert torch.all(torch.eq(states_proxy, states_proxy_expected_torch)) + + +@pytest.mark.parametrize( + "lattice_system, states, states_policy_expected", + [ + ( + TRICLINIC, + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.2, 0.5, 0.0, 0.5, 1.0], + [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + ], + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.2, 0.5, 0.0, 0.5, 1.0], + [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + ], + ), + ( + CUBIC, + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.25, 0.5, 0.75, 0.25, 0.5, 0.75], + [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + ], + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.25, 0.5, 0.75, 0.25, 0.5, 0.75], + [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + ], + ), + ], +) +def test__statetorch2policy__returns_expected( + env, lattice_system, states, states_policy_expected +): + """ + Various lattice systems are tried because the conversion should be independent of + the lattice system, since the states are expected to satisfy the constraints. + """ + # Get policy states from the batch of states converted into each subenv + # Get policy states from env.statetorch2policy + states_torch = tfloat(states, float_type=env.float, device=env.device) + states_policy_expected_torch = tfloat( + states_policy_expected, float_type=env.float, device=env.device + ) + states_policy = env.statetorch2policy(states_torch) + assert torch.all(torch.eq(states_policy, states_policy_expected_torch)) + states_policy = env.statebatch2policy(states_torch) + assert torch.all(torch.eq(states_policy, states_policy_expected_torch)) + + @pytest.mark.parametrize( "lattice_system, expected_output", [ From 533badf940901fc79d2082f04c7f6ae6aa9f0f2c Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 11:54:30 -0400 Subject: [PATCH 124/205] Fix in state2policy() --- gflownet/envs/crystals/ccrystal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 6db7a8dad..55abf839a 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -753,7 +753,7 @@ def state2policy(self, state: Optional[List[int]] = None) -> Tensor: state = self._get_state(state) return self.statetorch2policy( torch.unsqueeze(tfloat(state, device=self.device, float_type=self.float), 0) - ) + )[0] def statebatch2policy( self, states: List[List] From 00b17005d2f95d121d01f8b8a4a6e6c72c38e3b9 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 12:09:01 -0400 Subject: [PATCH 125/205] Fix recursion issue between state2proxy/state2oracle --- gflownet/envs/crystals/ccrystal.py | 44 +++++++++---------- gflownet/envs/crystals/clattice_parameters.py | 34 +++++++------- gflownet/envs/cube.py | 30 ++++++------- 3 files changed, 54 insertions(+), 54 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 55abf839a..a6ff0512b 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -781,63 +781,63 @@ def statetorch2policy( dim=1, ) - def state2proxy(self, state: Optional[List[int]] = None) -> Tensor: + def state2oracle(self, state: Optional[List[int]] = None) -> Tensor: """ - Prepares one state in "GFlowNet format" for the proxy. Simply + Prepares one state in "GFlowNet format" for the oracle. Simply a concatenation of all crystal components. """ state = self._get_state(state) - return self.statetorch2proxy( + return self.statetorch2oracle( torch.unsqueeze(tfloat(state, device=self.device, float_type=self.float), 0) ) - def statebatch2proxy( + def statebatch2oracle( self, states: List[List] - ) -> TensorType["batch", "state_proxy_dim"]: + ) -> TensorType["batch", "state_oracle_dim"]: """ - Prepares a batch of states in "GFlowNet format" for the proxy. Simply + Prepares a batch of states in "GFlowNet format" for the oracle. Simply a concatenation of all crystal components. """ - return self.statetorch2proxy( + return self.statetorch2oracle( tfloat(states, device=self.device, float_type=self.float) ) - def statetorch2proxy( + def statetorch2oracle( self, states: TensorType["batch", "state_dim"] - ) -> TensorType["batch", "state_proxy_dim"]: + ) -> TensorType["batch", "state_oracle_dim"]: """ - Prepares one state in "GFlowNet format" for the proxy. Simply + Prepares one state in "GFlowNet format" for the oracle. Simply a concatenation of all crystal components. """ return torch.cat( [ - subenv.statetorch2proxy(self._get_states_of_subenv(states, stage)) + subenv.statetorch2oracle(self._get_states_of_subenv(states, stage)) for stage, subenv in self.subenvs.items() ], dim=1, ) - def state2oracle(self, state: Optional[List[int]] = None) -> Tensor: + def state2proxy(self, state: Optional[List[int]] = None) -> Tensor: """ - Returns state2proxy(state). + Returns state2oracle(state). """ - return self.state2proxy(state) + return self.state2oracle(state) - def statebatch2oracle( + def statebatch2proxy( self, states: List[List] - ) -> TensorType["batch", "state_proxy_dim"]: + ) -> TensorType["batch", "state_oracle_dim"]: """ - Returns statebatch2proxy(states). + Returns statebatch2oracle(states). """ - return self.statebatch2proxy(states) + return self.statebatch2oracle(states) - def statetorch2oracle( + def statetorch2proxy( self, states: TensorType["batch", "state_dim"] - ) -> TensorType["batch", "state_proxy_dim"]: + ) -> TensorType["batch", "state_oracle_dim"]: """ - Returns statetorch2proxy(states). + Returns statetorch2oracle(states). """ - return statetorch2proxy(states) + return self.statetorch2oracle(states) def set_state(self, state: List, done: Optional[bool] = False): super().set_state(state, done) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index ad8091a8c..0d1484001 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -298,7 +298,7 @@ def statetorch2policy( """ return states - def state2proxy(self, state: Optional[List[float]] = None) -> Tensor: + def state2oracle(self, state: Optional[List[float]] = None) -> Tensor: """ Maps [0; 1] state values to edge lengths and angles. """ @@ -310,19 +310,19 @@ def state2proxy(self, state: Optional[List[float]] = None) -> Tensor: device=self.device, ) - def statebatch2proxy( + def statebatch2oracle( self, states: List[List] - ) -> TensorType["batch", "state_proxy_dim"]: + ) -> TensorType["batch", "state_oracle_dim"]: """ Maps [0; 1] state values to edge lengths and angles. """ - return self.statetorch2proxy( + return self.statetorch2oracle( tfloat(states, device=self.device, float_type=self.float) ) - def statetorch2proxy( + def statetorch2oracle( self, states: TensorType["batch", "state_dim"] = None - ) -> TensorType["batch", "proxy_input_dim"]: + ) -> TensorType["batch", "oracle_input_dim"]: """ Maps [0; 1] state values to edge lengths and angles. """ @@ -334,24 +334,24 @@ def statetorch2proxy( dim=1, ) - def state2oracle(self, state: Optional[List[int]] = None) -> Tensor: + def state2proxy(self, state: Optional[List[int]] = None) -> Tensor: """ - Returns state2proxy(state). + Returns state2oracle(state). """ - return self.state2proxy(state) + return self.state2oracle(state) - def statebatch2oracle( + def statebatch2proxy( self, states: List[List] - ) -> TensorType["batch", "state_proxy_dim"]: + ) -> TensorType["batch", "state_oracle_dim"]: """ - Returns statebatch2proxy(states). + Returns statebatch2oracle(states). """ - return self.statebatch2proxy(states) + return self.statebatch2oracle(states) - def statetorch2oracle( + def statetorch2proxy( self, states: TensorType["batch", "state_dim"] - ) -> TensorType["batch", "state_proxy_dim"]: + ) -> TensorType["batch", "state_oracle_dim"]: """ - Returns statetorch2proxy(states). + Returns statetorch2oracle(states). """ - return statetorch2proxy(states) + return self.statetorch2oracle(states) diff --git a/gflownet/envs/cube.py b/gflownet/envs/cube.py index c5d508961..4ad2a82ed 100644 --- a/gflownet/envs/cube.py +++ b/gflownet/envs/cube.py @@ -132,9 +132,9 @@ def get_mask_invalid_actions_forward( def get_mask_invalid_actions_backward(self, state=None, done=None, parents_a=None): pass - def statetorch2proxy( + def statetorch2oracle( self, states: TensorType["batch", "state_dim"] = None - ) -> TensorType["batch", "proxy_input_dim"]: + ) -> TensorType["batch", "oracle_input_dim"]: """ Clips the states into [0, 1] and maps them to [-1.0, 1.0] @@ -145,9 +145,9 @@ def statetorch2proxy( """ return 2.0 * torch.clip(states, min=0.0, max=1.0) - 1.0 - def statebatch2proxy( + def statebatch2oracle( self, states: List[List] - ) -> TensorType["batch", "state_proxy_dim"]: + ) -> TensorType["batch", "state_oracle_dim"]: """ Clips the states into [0, 1] and maps them to [-1.0, 1.0] @@ -156,11 +156,11 @@ def statebatch2proxy( state : list State """ - return self.statetorch2proxy( + return self.statetorch2oracle( tfloat(states, device=self.device, float_type=self.float) ) - def state2proxy(self, state: List = None) -> List: + def state2oracle(self, state: List = None) -> List: """ Clips the state into [0, 1] and maps it to [-1.0, 1.0] """ @@ -168,37 +168,37 @@ def state2proxy(self, state: List = None) -> List: state = self.state.copy() return [2.0 * min(max(0.0, s), 1.0) - 1.0 for s in state] - def statetorch2oracle( + def statetorch2proxy( self, states: TensorType["batch", "state_dim"] = None ) -> TensorType["batch", "oracle_input_dim"]: """ - Returns statetorch2proxy(states), that is states mapped to [-1.0, 1.0]. + Returns statetorch2oracle(states), that is states mapped to [-1.0, 1.0]. Args ---- state : list State """ - return self.statetorch2proxy(states) + return self.statetorch2oracle(states) - def statebatch2oracle( + def statebatch2proxy( self, states: List[List] ) -> TensorType["batch", "state_oracle_dim"]: """ - Returns statebatch2proxy(states), that is states mapped to [-1.0, 1.0]. + Returns statebatch2oracle(states), that is states mapped to [-1.0, 1.0]. Args ---- state : list State """ - return self.statebatch2proxy(states) + return self.statebatch2oracle(states) - def state2oracle(self, state: List = None) -> List: + def state2proxy(self, state: List = None) -> List: """ - Returns state2proxy(state), that is the state mapped to [-1.0, 1.0]. + Returns state2oracle(state), that is the state mapped to [-1.0, 1.0]. """ - return self.state2proxy(state) + return self.state2oracle(state) def statetorch2policy( self, states: TensorType["batch", "state_dim"] = None From 31327edffac171ffa0d88ecb059ecd1ee6df6b55 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 12:32:38 -0400 Subject: [PATCH 126/205] Fix state2readable --- gflownet/envs/crystals/ccrystal.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index a6ff0512b..d5ddd45a9 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -878,17 +878,13 @@ def state2readable(self, state: Optional[List[int]] = None) -> str: readables = [ subenv.state2readable(self._get_state_of_subenv(state, stage)) - for stage, subenv in self.subenvs + for stage, subenv in self.subenvs.items() ] - composition_readable = readables[0] - space_group_readable = readables[1] - lattice_parameters_readable = readables[2] - return ( - f"Stage = {state[0]}; " - f"Composition = {composition_readable}; " - f"SpaceGroup = {space_group_readable}; " - f"LatticeParameters = {lattice_parameters_readable}" + f"{self._get_stage(state)}; " + f"Composition = {readables[0]}; " + f"SpaceGroup = {readables[1]}; " + f"LatticeParameters = {readables[2]}" ) # TODO: redo From 6434694706fdc6655931e1fb07832e2f64c30af9 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 12:33:03 -0400 Subject: [PATCH 127/205] Add test readable --- tests/gflownet/envs/test_ccrystal.py | 41 ++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 21038c3ef..7c8bd2e26 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -181,6 +181,47 @@ def test__statetorch2proxy__is_concatenation_of_subenv_states(env, states): assert torch.all(torch.eq(states_proxy, states_proxy_expected)) +@pytest.mark.parametrize( + "states", + [ + [ + [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.12, 0.23, 0.34, 0.45, 0.56, 0.67], + [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], + [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + [0, 0, 4, 3, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + ], + ], +) +def test__state2readable__is_concatenation_of_subenv_states(env, states): + # Get policy states from the batch of states converted into each subenv + states_readable_expected = [] + for state in states: + readables = [] + for stage, subenv in env.subenvs.items(): + readables.append( + subenv.state2readable(env._get_state_of_subenv(state, stage)) + ) + states_readable_expected.append( + f"{env._get_stage(state)}; " + f"Composition = {readables[0]}; " + f"SpaceGroup = {readables[1]}; " + f"LatticeParameters = {readables[2]}" + ) + # Get policy states from env.statetorch2policy + states_readable = [env.state2readable(state) for state in states] + for readable, readable_expected in zip(states_readable, states_readable_expected): + assert readable == readable_expected + + @pytest.mark.parametrize( "state, state_composition, state_space_group, state_lattice_parameters", [ From bd87257e07674a04648ef83a836739be9c2d57b0 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 12:33:14 -0400 Subject: [PATCH 128/205] Add TODOs --- gflownet/envs/crystals/clattice_parameters.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index 0d1484001..7efca40e0 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -84,12 +84,14 @@ def __init__( self._setup_constraints() super().__init__(n_dim=6, **kwargs) + # TODO: if source, keep as is def _statevalue2length(self, value): return self.min_length + value * self.length_range def _length2statevalue(self, length): return (length - self.min_length) / self.length_range + # TODO: if source, keep as is def _statevalue2angle(self, value): return self.min_angle + value * self.angle_range From d734402b49e70f4cb81f3dd13d0dc69088e26443 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 15:56:57 -0400 Subject: [PATCH 129/205] ccrystal config file --- config/env/crystals/ccrystal.yaml | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 config/env/crystals/ccrystal.yaml diff --git a/config/env/crystals/ccrystal.yaml b/config/env/crystals/ccrystal.yaml new file mode 100644 index 000000000..ca3c7b288 --- /dev/null +++ b/config/env/crystals/ccrystal.yaml @@ -0,0 +1,26 @@ +defaults: + - base + +_target_: gflownet.envs.crystals.ccrystal.CCrystal + +# Composition config +id: ccrystal +composition_kwargs: + elements: 89 +# Lattice parameters config +lattice_parameters_kwargs: + min_length: 1.0 + max_length: 350.0 + min_angle: 50.0 + max_angle: 150.0 +# Space group config +space_group_kwargs: + space_groups_subset: null +# Stoichiometry <-> space group check +do_stoichiometry_sg_check: True + +# Buffer +buffer: + data_path: null + train: null + test: nulll From ea9db8c450040eb6ef47633dd7a98c475c7640e9 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Tue, 26 Sep 2023 15:58:10 -0400 Subject: [PATCH 130/205] improve dave docstring AND fix sg-1 --- gflownet/proxy/crystals/dave.py | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/gflownet/proxy/crystals/dave.py b/gflownet/proxy/crystals/dave.py index 2a111ffde..e560d3143 100644 --- a/gflownet/proxy/crystals/dave.py +++ b/gflownet/proxy/crystals/dave.py @@ -98,10 +98,28 @@ def _set_scales(self): self.scaled = True @torch.no_grad() - def __call__(self, states: TensorType["batch", "96"]) -> TensorType["batch"]: + def __call__(self, states: TensorType["batch", "102"]) -> TensorType["batch"]: """ Forward pass of the proxy. + The proxy will decompose the state as: + * composition: ``states[:, :-7]`` -> length 95 (dummy 0 then 94 elements) + * space group: ``states[:, -7] - 1`` + * lattice parameters: ``states[:, -6:]`` + + >>> composition MUST be a list of ATOMIC NUMBERS, prepended with a 0. + >>> dummy padding value at comp[0] MUST be 0. + ie -> comp[i] -> element Z=i + ie -> LiO2 -> [0, 0, 0, 1, 0, 0, 2, 0, ...] up until Z=94 for the MatBench proxy + ie -> len(comp) = 95 (0 then 94 elements) + + >>> sg MUST be a list of ACTUAL space group numbers (1-230) + + >>> lat_params MUST be a list of lattice parameters in the following order: + [a, b, c, alpha, beta, gamma] as floats. + + >>> the states tensor MUST already be on the device. + Args: states (torch.Tensor): States to infer on. Shape: ``(batch, [6 + 1 + n_elements])``. @@ -112,7 +130,7 @@ def __call__(self, states: TensorType["batch", "96"]) -> TensorType["batch"]: self._set_scales() comp = states[:, :-7] - sg = states[:, -7] - 1 + sg = states[:, -7] lat_params = states[:, -6:] if self.rescale_outputs: From 7f2da073eab7c5c65e42340bcd7e526d7d6205da Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 16:20:56 -0400 Subject: [PATCH 131/205] Remove assert all(valids) when sampling backward trajectories - hopefully it is ok --- gflownet/gflownet.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gflownet/gflownet.py b/gflownet/gflownet.py index ba3d9f635..c097cdca8 100644 --- a/gflownet/gflownet.py +++ b/gflownet/gflownet.py @@ -756,7 +756,6 @@ def estimate_logprobs_data( ) # Update environments with sampled actions envs, actions, valids = self.step(envs, actions, backward=True) - assert all(valids) # Add to batch batch.add_to_batch(envs, actions, valids, backward=True, train=True) # Filter out finished trajectories From f81575eeb563ad260e0f2b7a450332abd8ec612c Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 16:28:54 -0400 Subject: [PATCH 132/205] Change state2oracle methods of composition --- gflownet/envs/crystals/composition.py | 29 ++++++++++++++++++--------- 1 file changed, 20 insertions(+), 9 deletions(-) diff --git a/gflownet/envs/crystals/composition.py b/gflownet/envs/crystals/composition.py index 2e1e75240..46fb2b3c9 100644 --- a/gflownet/envs/crystals/composition.py +++ b/gflownet/envs/crystals/composition.py @@ -20,6 +20,8 @@ space_group_wyckoff_gcd, ) +N_ELEMENTS_ORACLE = 94 + class Composition(GFlowNetEnv): """ @@ -28,7 +30,7 @@ class Composition(GFlowNetEnv): def __init__( self, - elements: Union[List, int] = 84, + elements: Union[List, int] = 94, max_diff_elem: int = 5, min_diff_elem: int = 2, min_atoms: int = 2, @@ -406,8 +408,9 @@ def get_element_mask(min_atoms, max_atoms): def state2oracle(self, state: List = None) -> Tensor: """ - Prepares a state in "GFlowNet format" for the oracle. In this case, it simply - converts the state into a torch tensor, with dtype torch.long. + Prepares a state in "GFlowNet format" for the oracle. The output is a tensor of + length N_ELEMENTS_ORACLE + 1, where the positions of self.elements are filled with + the number of atoms of each element in the state. Args ---- @@ -421,15 +424,17 @@ def state2oracle(self, state: List = None) -> Tensor: """ if state is None: state = self.state - - return tlong(state, device=self.device) + return self.statetorch2oracle( + torch.unsqueeze(tfloat(states, device=self.device), 0) + ) def statetorch2oracle( self, states: TensorType["batch", "state_dim"] ) -> TensorType["batch", "state_oracle_dim"]: """ - Prepares a batch of states in "GFlowNet format" for the oracle. The input to the - oracle is the atom counts for individual elements. + Prepares a batch of states in "GFlowNet format" for the oracle. The output is + a tensor with N_ELEMENTS_ORACLE + 1 columns, where the positions of + self.elements are filled with the number of atoms of each element in the state. Args ---- @@ -440,7 +445,13 @@ def statetorch2oracle( ---- oracle_states : Tensor """ - return states + states_oracle = torch.zeros( + (states.shape[0], N_ELEMENTS_ORACLE + 1), + device=self.device, + dtype=self.float, + ) + states_oracle[:, tlong(self.elements, device=self.device)] = states + return states_oracle def statebatch2oracle( self, states: List[List] @@ -453,7 +464,7 @@ def statebatch2oracle( ---- state : list """ - return tlong(states, device=self.device) + return self.statetorch2oracle(tlong(states, device=self.device)) def state2readable(self, state=None): """ From 19726f465ac4d5a1e0d70037d7349c818e9fbc92 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 16:57:43 -0400 Subject: [PATCH 133/205] import tflaot --- gflownet/envs/crystals/composition.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/composition.py b/gflownet/envs/crystals/composition.py index 46fb2b3c9..ce5eadb2b 100644 --- a/gflownet/envs/crystals/composition.py +++ b/gflownet/envs/crystals/composition.py @@ -11,7 +11,7 @@ from torchtyping import TensorType from gflownet.envs.base import GFlowNetEnv -from gflownet.utils.common import tlong +from gflownet.utils.common import tfloat, tlong from gflownet.utils.crystals.constants import ELEMENT_NAMES, OXIDATION_STATES from gflownet.utils.crystals.pyxtal_cache import ( get_space_group, From 406f18b004f2f2130559fd6fe4758927ccf40c18 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Tue, 26 Sep 2023 17:01:11 -0400 Subject: [PATCH 134/205] auto parse repo name --- mila/launch.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/mila/launch.py b/mila/launch.py index 19d67bd55..d2f3d885b 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -401,17 +401,21 @@ def code_dir_for_slurm_tmp_dir_checkout(git_checkout): sys.exit(0) GIT_WARNING = False + repo_url = ssh_to_https(repo.remotes.origin.url) + repo_name = repo_url.split("/")[-1].split(".git")[0] + return dedent( """\ $SLURM_TMPDIR - git clone {git_url} tpm-gflownet - cd tpm-gflownet + git clone {git_url} tmp-{repo_name} + cd tmp-{repo_name} {git_checkout} echo "Current commit: $(git rev-parse HEAD)" """ ).format( - git_url=ssh_to_https(repo.remotes.origin.url), + git_url=repo_url, git_checkout=f"git checkout {git_checkout}" if git_checkout else "", + repo_name=repo_name, ) From 54eea8fc1e55b82aca390ad521e82376665907a7 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Tue, 26 Sep 2023 17:01:51 -0400 Subject: [PATCH 135/205] bump version --- config/proxy/crystals/dave.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/proxy/crystals/dave.yaml b/config/proxy/crystals/dave.yaml index 20865c20c..c00efc37f 100644 --- a/config/proxy/crystals/dave.yaml +++ b/config/proxy/crystals/dave.yaml @@ -1,6 +1,6 @@ _target_: gflownet.proxy.crystals.dave.DAVE -release: 0.3.2 +release: 0.3.3 ckpt_path: mila: /network/scratch/s/schmidtv/crystals-proxys/proxy-ckpts/ victor: ~/Documents/Github/ActiveLearningMaterials/checkpoints/980065c0/checkpoints-3ff648a2 From 16b85eb23dbb3e4a4e94537c66a93d587a829249 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Tue, 26 Sep 2023 17:04:04 -0400 Subject: [PATCH 136/205] real space group, not index (no `-1`) --- gflownet/proxy/crystals/dave.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/proxy/crystals/dave.py b/gflownet/proxy/crystals/dave.py index d261e6b90..26aaefded 100644 --- a/gflownet/proxy/crystals/dave.py +++ b/gflownet/proxy/crystals/dave.py @@ -112,7 +112,7 @@ def __call__(self, states: TensorType["batch", "96"]) -> TensorType["batch"]: self._set_scales() comp = states[:, :-7] - sg = states[:, -7] - 1 + sg = states[:, -7] lat_params = states[:, -6:] n_env = comp.shape[-1] From 3fba801fcdd87ccee8caa9a5306dcabb581d3d7e Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 17:04:19 -0400 Subject: [PATCH 137/205] Allow passing data set as pkl or csv --- gflownet/gflownet.py | 2 +- gflownet/utils/buffer.py | 12 +++++++----- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/gflownet/gflownet.py b/gflownet/gflownet.py index c097cdca8..74a87b34f 100644 --- a/gflownet/gflownet.py +++ b/gflownet/gflownet.py @@ -1024,7 +1024,7 @@ def test(self, **plot_kwargs): (None,), env_metrics, ) - elif self.continuous: + elif self.continuous and hasattr(self.env, "fit_kde"): # TODO make it work with conditional env x_sampled = torch2np(self.env.statebatch2proxy(x_sampled)) x_tt = torch2np(self.env.statebatch2proxy(x_tt)) diff --git a/gflownet/utils/buffer.py b/gflownet/utils/buffer.py index c66f4d8d3..d791f1f8f 100644 --- a/gflownet/utils/buffer.py +++ b/gflownet/utils/buffer.py @@ -200,13 +200,15 @@ def make_data_set(self, config): """ if config is None: return None, None - elif "path" in config and config.path is not None: - path = self.logger.logdir / Path("data") / config.path - df = pd.read_csv(path, index_col=0) - # TODO: check if state2readable transformation is required. - return df elif "type" not in config: return None, None + elif config.type == "pkl" and "path" in config: + with open(config.path, "rb") as f: + data_dict = pickle.load(f) + samples = data_dict["x"] + elif config.type == "csv" and "path" in config: + df = pd.read_csv(config.path, index_col=0) + samples = df.iloc[:, :-1].values elif config.type == "all" and hasattr(self.env, "get_all_terminating_states"): samples = self.env.get_all_terminating_states() elif ( From d512a972b00244329766889ec9f735697a42b023 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Tue, 26 Sep 2023 17:05:31 -0400 Subject: [PATCH 138/205] add sample val data Matbench val split top 12 elements only train & val spacegroups --- .../matbench_val_12_SGinter_states_energy.pkl | Bin 0 -> 228140 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 data/crystals/matbench_val_12_SGinter_states_energy.pkl diff 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zst0Y7bu<)0*L@k)qqUXf-yc8yns(C(n(}5#*IA}JadPDOEh}nhfTK+BQEXJu6)(IWG9Ua~vfjo755L7~BjmMbH)7xaFLea^Ua-6vVA z7~8V1jda9~KL~)UI_QR{j^9{5_Y-!b zIxSxBco1yeC#eBW0oQk7E!)l0Gv97R%$Ae4_~11qv^?|O(j%D?7zfU!&yzcH!)jo$ z*KECLrT9}&MaMypn_bxpc!z|Sch?!5hoVV$3H|zb&k;Jgoq|}p;nJzrQ5Jl_DG zUf!>gHE|}8zkGaO=ch0!-xTOez7uc4LUMhRW@Vr{mpb>_we{f`BeWy3@}4zhqf|pI zdhD{EekMTn>BD*4j#Xg7qD7nUTdROAx4Zwkmi7@2Cwtq$z)8oEsnkTy`4F8AIttdb zcNGPr3eI%(*V_3SgOC3dwkKNBXfS17;xMZU^z9ZALRFOyTup&RD;%ybK_K&J%;>RbR2zCJrx`LJ8gEVd#(3= zJ9A9c4}a*T#PJ7eKfy7Hx5U@xX69*s>=i+931* z7@iU-?%Vb@CNzi$$lri-s>hmVE`OV(PIFFmIAyo{^bDX8y=0DzBnNcawRvx)2Zjqp ze=cpa{S=-$yXV>F!VB0ap${jj9xnh7$E@}?=@;$`w=p|<@aVs`Ybwb^oO4!ugo%(^ZzGd92& zY+AAXMwIOc+|yZn&3u-e3q(S1MfrEDQSfrX+lQa?fAaqZbePGSR1eV|z|hE<$Xi!) z99XqVd3S}B-T<~HJl(^tEz1MWoNF1*N|tj5CV<`9tgdH)<03!nypQ-TVFb>=U+IgR zxgMA{K5Y8DHT}*fVD70^58mZ9)qQ`PrlN)^aJ16?m)P>$N3(i>F7%%M Date: Tue, 26 Sep 2023 17:17:55 -0400 Subject: [PATCH 139/205] minor release: 0.4.0 --- config/proxy/crystals/dave.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/proxy/crystals/dave.yaml b/config/proxy/crystals/dave.yaml index 20865c20c..b9eab1d7b 100644 --- a/config/proxy/crystals/dave.yaml +++ b/config/proxy/crystals/dave.yaml @@ -1,6 +1,6 @@ _target_: gflownet.proxy.crystals.dave.DAVE -release: 0.3.2 +release: 0.4.0 ckpt_path: mila: /network/scratch/s/schmidtv/crystals-proxys/proxy-ckpts/ victor: ~/Documents/Github/ActiveLearningMaterials/checkpoints/980065c0/checkpoints-3ff648a2 From 0cc80ae5fd54f945ac5d39ce9fc70348a6fb6db4 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Tue, 26 Sep 2023 17:22:55 -0400 Subject: [PATCH 140/205] 0.3.3 --- config/proxy/crystals/dave.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/proxy/crystals/dave.yaml b/config/proxy/crystals/dave.yaml index b9eab1d7b..c00efc37f 100644 --- a/config/proxy/crystals/dave.yaml +++ b/config/proxy/crystals/dave.yaml @@ -1,6 +1,6 @@ _target_: gflownet.proxy.crystals.dave.DAVE -release: 0.4.0 +release: 0.3.3 ckpt_path: mila: /network/scratch/s/schmidtv/crystals-proxys/proxy-ckpts/ victor: ~/Documents/Github/ActiveLearningMaterials/checkpoints/980065c0/checkpoints-3ff648a2 From 7380865b59c99e875ff7bc569a0877ed2e77c3dd Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Tue, 26 Sep 2023 17:32:53 -0400 Subject: [PATCH 141/205] bump dave version --- config/proxy/crystals/dave.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/proxy/crystals/dave.yaml b/config/proxy/crystals/dave.yaml index c00efc37f..06c4eb2a2 100644 --- a/config/proxy/crystals/dave.yaml +++ b/config/proxy/crystals/dave.yaml @@ -1,6 +1,6 @@ _target_: gflownet.proxy.crystals.dave.DAVE -release: 0.3.3 +release: 0.3.4 ckpt_path: mila: /network/scratch/s/schmidtv/crystals-proxys/proxy-ckpts/ victor: ~/Documents/Github/ActiveLearningMaterials/checkpoints/980065c0/checkpoints-3ff648a2 From d809c5a5a839a80b99e05109306907e4d7dc19e7 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 18:06:58 -0400 Subject: [PATCH 142/205] hopefully minor changes in test() --- gflownet/gflownet.py | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/gflownet/gflownet.py b/gflownet/gflownet.py index 74a87b34f..4819bee5c 100644 --- a/gflownet/gflownet.py +++ b/gflownet/gflownet.py @@ -1011,19 +1011,6 @@ def test(self, **plot_kwargs): density_pred = np.array([hist[tuple(x)] / z_pred for x in x_tt]) log_density_true = np.log(density_true + 1e-8) log_density_pred = np.log(density_pred + 1e-8) - elif self.buffer.test_type == "random": - # TODO: refactor - env_metrics = self.env.test(x_sampled) - return ( - self.l1, - self.kl, - self.jsd, - corr_prob_traj_rewards, - var_logrewards_logp, - nll_tt, - (None,), - env_metrics, - ) elif self.continuous and hasattr(self.env, "fit_kde"): # TODO make it work with conditional env x_sampled = torch2np(self.env.statebatch2proxy(x_sampled)) @@ -1064,7 +1051,18 @@ def test(self, **plot_kwargs): density_true = np.exp(log_density_true) density_pred = np.exp(log_density_pred) else: - raise NotImplementedError + # TODO: refactor + env_metrics = self.env.test(x_sampled) + return ( + self.l1, + self.kl, + self.jsd, + corr_prob_traj_rewards, + var_logrewards_logp, + nll_tt, + (None,), + env_metrics, + ) # L1 error l1 = np.abs(density_pred - density_true).mean() # KL divergence From da752129199de77fc141c342dc27c1af8ac2be36 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 18:07:13 -0400 Subject: [PATCH 143/205] Add prints --- gflownet/utils/buffer.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/gflownet/utils/buffer.py b/gflownet/utils/buffer.py index d791f1f8f..a6640bd09 100644 --- a/gflownet/utils/buffer.py +++ b/gflownet/utils/buffer.py @@ -200,13 +200,16 @@ def make_data_set(self, config): """ if config is None: return None, None - elif "type" not in config: + print("\nConstructing data set ", end="") + if "type" not in config: return None, None elif config.type == "pkl" and "path" in config: + print(f"from pickled file: {config.path}\n") with open(config.path, "rb") as f: data_dict = pickle.load(f) samples = data_dict["x"] elif config.type == "csv" and "path" in config: + print(f"from CSV: {config.path}\n") df = pd.read_csv(config.path, index_col=0) samples = df.iloc[:, :-1].values elif config.type == "all" and hasattr(self.env, "get_all_terminating_states"): @@ -216,6 +219,7 @@ def make_data_set(self, config): and "n" in config and hasattr(self.env, "get_grid_terminating_states") ): + print(f"by sampling a grid of {config.n} points\n") samples = self.env.get_grid_terminating_states(config.n) elif ( config.type == "uniform" @@ -223,12 +227,14 @@ def make_data_set(self, config): and "seed" in config and hasattr(self.env, "get_uniform_terminating_states") ): + print(f"by sampling {config.n} points uniformly\n") samples = self.env.get_uniform_terminating_states(config.n, config.seed) elif ( config.type == "random" and "n" in config and hasattr(self.env, "get_random_terminating_states") ): + print(f"by sampling {config.n} points randomly\n") samples = self.env.get_random_terminating_states(config.n) else: return None, None From b6e395db76b22319aaf2dd036182995e80714cde Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 18:07:29 -0400 Subject: [PATCH 144/205] dave release 0.3.4 --- config/proxy/crystals/dave.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config/proxy/crystals/dave.yaml b/config/proxy/crystals/dave.yaml index c00efc37f..06c4eb2a2 100644 --- a/config/proxy/crystals/dave.yaml +++ b/config/proxy/crystals/dave.yaml @@ -1,6 +1,6 @@ _target_: gflownet.proxy.crystals.dave.DAVE -release: 0.3.3 +release: 0.3.4 ckpt_path: mila: /network/scratch/s/schmidtv/crystals-proxys/proxy-ckpts/ victor: ~/Documents/Github/ActiveLearningMaterials/checkpoints/980065c0/checkpoints-3ff648a2 From 231665ade4efd9af7cf0a9051247baed0b8db93f 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zst0Y7bu<)0*L@k)qqUXf-yc8yns(C(n(}5#*IA}JadPDOEh}nhfTK+BQEXJu6)(IWG9Ua~vfjo755L7~BjmMbH)7xaFLea^Ua-6vVA z7~8V1jda9~KL~)UI_QR{j^9{5_Y-!b zIxSxBco1yeC#eBW0oQk7E!)l0Gv97R%$Ae4_~11qv^?|O(j%D?7zfU!&yzcH!)jo$ z*KECLrT9}&MaMypn_bxpc!z|Sch?!5hoVV$3H|zb&k;Jgoq|}p;nJzrQ5Jl_DG zUf!>gHE|}8zkGaO=ch0!-xTOez7uc4LUMhRW@Vr{mpb>_we{f`BeWy3@}4zhqf|pI zdhD{EekMTn>BD*4j#Xg7qD7nUTdROAx4Zwkmi7@2Cwtq$z)8oEsnkTy`4F8AIttdb zcNGPr3eI%(*V_3SgOC3dwkKNBXfS17;xMZU^z9ZALRFOyTup&RD;%ybK_K&J%;>RbR2zCJrx`LJ8gEVd#(3= zJ9A9c4}a*T#PJ7eKfy7Hx5U@xX69*s>=i+931* z7@iU-?%Vb@CNzi$$lri-s>hmVE`OV(PIFFmIAyo{^bDX8y=0DzBnNcawRvx)2Zjqp ze=cpa{S=-$yXV>F!VB0ap${jj9xnh7$E@}?=@;$`w=p|<@aVs`Ybwb^oO4!ugo%(^ZzGd92& zY+AAXMwIOc+|yZn&3u-e3q(S1MfrEDQSfrX+lQa?fAaqZbePGSR1eV|z|hE<$Xi!) z99XqVd3S}B-T<~HJl(^tEz1MWoNF1*N|tj5CV<`9tgdH)<03!nypQ-TVFb>=U+IgR zxgMA{K5Y8DHT}*fVD70^58mZ9)qQ`PrlN)^aJ16?m)P>$N3(i>F7%%M Date: Tue, 26 Sep 2023 20:21:56 -0400 Subject: [PATCH 146/205] Cache spacegroup composition checks --- gflownet/envs/crystals/spacegroup.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/spacegroup.py b/gflownet/envs/crystals/spacegroup.py index 83713ddc0..eb54b4550 100644 --- a/gflownet/envs/crystals/spacegroup.py +++ b/gflownet/envs/crystals/spacegroup.py @@ -15,6 +15,7 @@ from torchtyping import TensorType from gflownet.envs.base import GFlowNetEnv +from gflownet.utils.crystals.pyxtal_cache import space_group_check_compatible CRYSTAL_LATTICE_SYSTEMS = None POINT_SYMMETRIES = None @@ -676,7 +677,7 @@ def build_n_atoms_compatibility_dict( n_atoms = [n for n in n_atoms if n > 0] assert all([n > 0 for n in n_atoms]) assert all([sg > 0 and sg <= 230 for sg in space_groups]) - return {sg: Group(sg).check_compatible(n_atoms)[0] for sg in space_groups} + return {sg: space_group_check_compatible(sg, n_atoms) for sg in space_groups} def _restrict_space_groups(self, sg_subset: Optional[Iterable] = None): """ From 7e35f31d4b37c40e5122b2c99b1f57901e86b206 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Tue, 26 Sep 2023 20:25:37 -0400 Subject: [PATCH 147/205] Update docstring --- gflownet/envs/crystals/spacegroup.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/gflownet/envs/crystals/spacegroup.py b/gflownet/envs/crystals/spacegroup.py index eb54b4550..6b6a9e105 100644 --- a/gflownet/envs/crystals/spacegroup.py +++ b/gflownet/envs/crystals/spacegroup.py @@ -10,7 +10,6 @@ import numpy as np import torch import yaml -from pyxtal.symmetry import Group from torch import Tensor from torchtyping import TensorType @@ -643,18 +642,17 @@ def _is_compatible( return len(space_groups) > 0 - # TODO: this method is quite slow, consider improving efficiency. @staticmethod def build_n_atoms_compatibility_dict( n_atoms: List[int], space_groups: Iterable[int] ): """ Obtains which space groups are compatible with the stoichiometry given as - argument (n_atoms). It relies on pyxtal's - pyxtal.symmetry.Group.check_compatible(). Note that True is stored only if both - is_compatible and has_freedom are True. + argument (n_atoms). - See: https://pyxtal.readthedocs.io/en/latest/pyxtal.symmetry.html + It relies on a function which, internally, calls pyxtal's + pyxtal.symmetry.Group.check_compatible(). Note that sometimes that pyxtal + is known to return invalid results. Args ---- From 9e500dfe33e8bd90a838c2f940374b4a40542863 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 21:36:32 -0400 Subject: [PATCH 148/205] Fixes and prints --- gflownet/gflownet.py | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/gflownet/gflownet.py b/gflownet/gflownet.py index 4819bee5c..89df00fcc 100644 --- a/gflownet/gflownet.py +++ b/gflownet/gflownet.py @@ -717,6 +717,7 @@ def estimate_logprobs_data( The logarithm of the average ratio PF/PB over n trajectories sampled for each data point. """ + print("Compute logprobs...", flush=True) batch = Batch(env=self.env, device=self.device, float_type=self.float) times = {} # Determine terminating states @@ -737,7 +738,7 @@ def estimate_logprobs_data( # Create an environment for each data point and trajectory and set the state envs = [] mult_indices = max(n_states, n_trajectories) - for state_idx, x in enumerate(states_term): + for state_idx, x in tqdm(enumerate(states_term)): for traj_idx in range(n_trajectories): idx = int(mult_indices * state_idx + traj_idx) env = self.env.copy().reset(idx) @@ -745,6 +746,10 @@ def estimate_logprobs_data( envs.append(env) # Sample trajectories max_iters = n_trajectories * max_iters_per_traj + print( + "Sampling backward actions from test data to estimate logprobs...", + flush=True, + ) while envs: # Sample backward actions actions = self.sample_actions( @@ -761,6 +766,7 @@ def estimate_logprobs_data( # Filter out finished trajectories envs = [env for env in envs if not env.equal(env.state, env.source)] # Prepare data structures to compute log probabilities + print("Done sampling backwards", flush=True) traj_indices_batch = tlong( batch.get_unique_trajectory_indices(), device=self.device ) @@ -789,6 +795,7 @@ def estimate_logprobs_data( logprobs_estimates = torch.logsumexp( logprobs_f - logprobs_b, dim=1 ) - torch.log(torch.tensor(n_trajectories, device=self.device)) + print("Done computing logprobs", flush=True) return logprobs_estimates def train(self): @@ -990,11 +997,12 @@ def test(self, **plot_kwargs): ).item() nll_tt = -logprobs_x_tt.mean().item() - batch, _ = self.sample_batch(n_forward=self.logger.test.n, train=False) - assert batch.is_valid() - x_sampled = batch.get_terminating_states() - + x_sampled = [] if self.buffer.test_type is not None and self.buffer.test_type == "all": + batch, _ = self.sample_batch(n_forward=self.logger.test.n, train=False) + assert batch.is_valid() + x_sampled = batch.get_terminating_states() + if "density_true" in dict_tt: density_true = dict_tt["density_true"] else: @@ -1012,6 +1020,9 @@ def test(self, **plot_kwargs): log_density_true = np.log(density_true + 1e-8) log_density_pred = np.log(density_pred + 1e-8) elif self.continuous and hasattr(self.env, "fit_kde"): + batch, _ = self.sample_batch(n_forward=self.logger.test.n, train=False) + assert batch.is_valid() + x_sampled = batch.get_terminating_states() # TODO make it work with conditional env x_sampled = torch2np(self.env.statebatch2proxy(x_sampled)) x_tt = torch2np(self.env.statebatch2proxy(x_tt)) From f98bd45181fe16b3fc2dae2ff7289ce6a65b0f67 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 21:36:47 -0400 Subject: [PATCH 149/205] black --- mila/launch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mila/launch.py b/mila/launch.py index d2f3d885b..2d5cedcef 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -7,8 +7,8 @@ from os.path import expandvars from pathlib import Path from textwrap import dedent -from git import Repo +from git import Repo from yaml import safe_load ROOT = Path(__file__).resolve().parent.parent From 2e18dd544d6fc4a579f48dea045c9ec2aec81053 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 21:37:18 -0400 Subject: [PATCH 150/205] Functionality to process whether data from data set is valid according to environments. --- gflownet/envs/crystals/ccrystal.py | 13 +++++++++ gflownet/envs/crystals/clattice_parameters.py | 15 +++++++++++ gflownet/envs/crystals/composition.py | 27 +++++++++++++++++++ gflownet/envs/crystals/spacegroup.py | 9 +++++++ gflownet/utils/buffer.py | 14 ++++++++++ 5 files changed, 78 insertions(+) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index d5ddd45a9..80dd423d3 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -887,6 +887,19 @@ def state2readable(self, state: Optional[List[int]] = None) -> str: f"LatticeParameters = {readables[2]}" ) + def process_data_set(self, data: List[List]) -> List[List]: + is_valid_list = [] + for x in data: + is_valid_list.append( + all( + [ + subenv.is_valid(self._get_state_of_subenv(x, stage)) + for stage, subenv in self.subenvs.items() + ] + ) + ) + return [x for x, is_valid in zip(data, is_valid_list) if is_valid] + # TODO: redo diff --git a/gflownet/envs/crystals/clattice_parameters.py b/gflownet/envs/crystals/clattice_parameters.py index 7efca40e0..4891bf5ef 100644 --- a/gflownet/envs/crystals/clattice_parameters.py +++ b/gflownet/envs/crystals/clattice_parameters.py @@ -357,3 +357,18 @@ def statetorch2proxy( Returns statetorch2oracle(states). """ return self.statetorch2oracle(states) + + def is_valid(self, x: List) -> bool: + """ + Determines whether a state is valid, according to the attributes of the + environment. + """ + lengths, angles = self._unpack_lengths_angles(x) + # Check lengths + if any([l < self.min_length or l > self.max_length for l in lengths]): + return False + if any([l < self.min_angle or l > self.max_angle for l in angles]): + return False + + # If all checks are passed, return True + return True diff --git a/gflownet/envs/crystals/composition.py b/gflownet/envs/crystals/composition.py index ce5eadb2b..4fd4b8c92 100644 --- a/gflownet/envs/crystals/composition.py +++ b/gflownet/envs/crystals/composition.py @@ -641,3 +641,30 @@ def _can_produce_neutral_charge(self, state: Optional[List[int]] = None) -> bool ] return any(poss_charge_sum) + + def is_valid(self, x: List) -> bool: + """ + Determines whether a state is valid, according to the attributes of the + environment. + """ + # Check length is equal to number of elements + if len(x) != len(self.elements): + return False + # Check total number of atoms + n_atoms = sum(x) + if n_atoms < self.min_atoms: + return False + if n_atoms > self.max_atoms: + return False + # Check number element + if any([n < self.min_atom_i for n in x if n > 0]): + return False + if any([n > self.max_atom_i for n in x if n > 0]): + return False + # Check required elements + used_elements = [self.idx2elem[idx] for idx, n in enumerate(x) if n > 0] + if any(r not in used_elements for r in self.required_elements): + return False + + # If all checks are passed, return True + return True diff --git a/gflownet/envs/crystals/spacegroup.py b/gflownet/envs/crystals/spacegroup.py index 6b6a9e105..7a935db56 100644 --- a/gflownet/envs/crystals/spacegroup.py +++ b/gflownet/envs/crystals/spacegroup.py @@ -770,3 +770,12 @@ def get_all_terminating_states( continue all_x.append(self._set_constrained_properties([0, 0, sg])) return all_x + + def is_valid(self, x: List) -> bool: + """ + Determines whether a state is valid, according to the attributes of the + environment. + """ + if x[self.sg_idx] in self.space_groups: + return True + return False diff --git a/gflownet/utils/buffer.py b/gflownet/utils/buffer.py index a6640bd09..9f36788ed 100644 --- a/gflownet/utils/buffer.py +++ b/gflownet/utils/buffer.py @@ -208,6 +208,20 @@ def make_data_set(self, config): with open(config.path, "rb") as f: data_dict = pickle.load(f) samples = data_dict["x"] + n_samples_orig = len(samples) + print(f"The data set containts {n_samples_orig} samples", end="") + samples = self.env.process_data_set(samples) + n_samples_new = len(samples) + if n_samples_new != n_samples_orig: + print( + f", but only {n_samples_new} are valid according to the " + "environment settings. Invalid samples have been discarded." + ) + samples = samples[:20] + print("We are currently selecting only 20 samples") + print("Remember to write a function to normalise the data in code") + print("Max number of elements in data set has to match config") + print("Actually, write a function that contrasts the stats") elif config.type == "csv" and "path" in config: print(f"from CSV: {config.path}\n") df = pd.read_csv(config.path, index_col=0) From 02c63f6ffd0644e5b768add26fb8c2a9347820b6 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 22:47:06 -0400 Subject: [PATCH 151/205] Add missing check in is_valid of composition --- gflownet/envs/crystals/composition.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/gflownet/envs/crystals/composition.py b/gflownet/envs/crystals/composition.py index 4fd4b8c92..5db53f0db 100644 --- a/gflownet/envs/crystals/composition.py +++ b/gflownet/envs/crystals/composition.py @@ -663,6 +663,10 @@ def is_valid(self, x: List) -> bool: return False # Check required elements used_elements = [self.idx2elem[idx] for idx, n in enumerate(x) if n > 0] + if len(used_elements) < self.min_diff_elem: + return False + if len(used_elements) > self.max_diff_elem: + return False if any(r not in used_elements for r in self.required_elements): return False From 468672f6dd2a2bb544d42d4a27e8d495a64c9a97 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Tue, 26 Sep 2023 22:47:43 -0400 Subject: [PATCH 152/205] Print states that yield nan/inf logprobs --- gflownet/gflownet.py | 11 ++++++++++- gflownet/utils/buffer.py | 4 ++-- 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/gflownet/gflownet.py b/gflownet/gflownet.py index 89df00fcc..9b6f8f14f 100644 --- a/gflownet/gflownet.py +++ b/gflownet/gflownet.py @@ -507,7 +507,6 @@ def sample_batch( envs, actions, valids = self.step(envs, actions, backward=True) # Add to batch batch_replay.add_to_batch(envs, actions, valids, backward=True, train=train) - assert all(valids) # Filter out finished trajectories envs = [env for env in envs if not env.equal(env.state, env.source)] times["replay_actions"] = time.time() - t0_replay @@ -791,6 +790,16 @@ def estimate_logprobs_data( logprobs_b[data_indices, traj_indices] = self.compute_logprobs_trajectories( batch, backward=True ) + # Check whether logprobs are finite + all_logprobs_finite = torch.all( + torch.logical_and(torch.isfinite(logprobs_f), torch.isfinite(logprobs_b)), + dim=1, + ) + if not torch.all(all_logprobs_finite): + print("The following samples have yielded inf or nan logprobs:") + for state, is_finite in zip(states_term, all_logprobs_finite): + if not is_finite: + print(self.env.state2readable(state)) # Compute log of the average probabilities of the ratio PF / PB logprobs_estimates = torch.logsumexp( logprobs_f - logprobs_b, dim=1 diff --git a/gflownet/utils/buffer.py b/gflownet/utils/buffer.py index 9f36788ed..9a84f4600 100644 --- a/gflownet/utils/buffer.py +++ b/gflownet/utils/buffer.py @@ -217,8 +217,8 @@ def make_data_set(self, config): f", but only {n_samples_new} are valid according to the " "environment settings. Invalid samples have been discarded." ) - samples = samples[:20] - print("We are currently selecting only 20 samples") + samples = samples[:25] + print("We are currently selecting only 25 samples") print("Remember to write a function to normalise the data in code") print("Max number of elements in data set has to match config") print("Actually, write a function that contrasts the stats") From b73627ed976c8b0e5d7dc968d9b146c2cd374842 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Wed, 27 Sep 2023 00:08:54 -0400 Subject: [PATCH 153/205] remove arbitrary filtering of samples --- gflownet/utils/buffer.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/gflownet/utils/buffer.py b/gflownet/utils/buffer.py index 9a84f4600..b5d3d3e42 100644 --- a/gflownet/utils/buffer.py +++ b/gflownet/utils/buffer.py @@ -217,8 +217,6 @@ def make_data_set(self, config): f", but only {n_samples_new} are valid according to the " "environment settings. Invalid samples have been discarded." ) - samples = samples[:25] - print("We are currently selecting only 25 samples") print("Remember to write a function to normalise the data in code") print("Max number of elements in data set has to match config") print("Actually, write a function that contrasts the stats") From 41e8864f2ed3d71a1ac6466400bbd30e9626bfc9 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Wed, 27 Sep 2023 00:09:21 -0400 Subject: [PATCH 154/205] add goose config --- config/experiments/crystals/goose.yaml | 85 ++++++++++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 config/experiments/crystals/goose.yaml diff --git a/config/experiments/crystals/goose.yaml b/config/experiments/crystals/goose.yaml new file mode 100644 index 000000000..37e331521 --- /dev/null +++ b/config/experiments/crystals/goose.yaml @@ -0,0 +1,85 @@ +# @package _global_ + +defaults: + - override /env: crystals/ccrystal + - override /gflownet: trajectorybalance + - override /proxy: crystals/dave + - override /logger: wandb + +device: cpu + +# Environment +env: + composition_kwargs: + elements: [1, 3, 6, 7, 8, 9, 12, 14, 15, 16, 17, 26] + max_atoms: 50 + max_atom_i: 16 + space_group_kwargs: + space_groups_subset: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 25, 26, 29, 30, 31, 33, 36, 38, 40, 41, 43, 44, 46, 47, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 82, 84, 85, 86, 87, 88, 92, 99, 102, 107, 113, 114, 121, 122, 123, 126, 129, 131, 136, 137, 138, 139, 140, 141, 146, 147, 148, 150, 155, 156, 160, 161, 162, 163, 164, 166, 167, 176, 181, 185, 186, 187, 189, 192, 194, 198, 199, 205, 206, 216, 217, 220, 221, 224, 225, 227, 229, 230] + lattice_parameters_kwargs: + min_length: 0.9 + max_length: 100.0 + min_angle: 50.0 + max_angle: 150.0 + reward_func: boltzmann + reward_beta: 1 + buffer: + replay_capacity: 0 + test: + type: pkl + path: /home/mila/h/hernanga/gflownet/data/crystals/matbench_normed_l0.9-100_a50-150_val_12_SGinter_states_energy.pkl + output_csv: ccrystal_val.csv + output_pkl: ccrystal_val.pkl + +# GFlowNet hyperparameters +gflownet: + random_action_prob: 0.1 + optimizer: + batch_size: + forward: 10 + backward_replay: -1 + lr: 0.001 + z_dim: 16 + lr_z_mult: 100 + n_train_steps: 10000 + lr_decay_period: 1000000 + replay_sampling: weighted + policy: + forward: + type: mlp + n_hid: 512 + n_layers: 5 + checkpoint: forward + backward: + type: mlp + n_hid: 512 + n_layers: 5 + shared_weights: False + checkpoint: backward + +# WandB +logger: + lightweight: True + project_name: "crystal-gfn" + tags: + - gflownet + - crystals + - matbench + - workshop23 + checkpoints: + period: 500 + do: + online: true + test: + n_trajs_logprobs: 10 + period: 100 + n: 10 + n_top_k: 5000 + top_k: 100 + top_k_period: -1 + +# Hydra +hydra: + run: + dir: ${user.logdir.root}/workshop23/discrete-matbench/${oc.env:SLURM_JOB_ID,local}/${now:%Y-%m-%d_%H-%M-%S} + From ed14ce456b9501cdee7e51f3d215e7a0163948a8 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 28 Sep 2023 21:12:49 -0400 Subject: [PATCH 155/205] penguin --- config/experiments/crystals/penguin.yaml | 103 +++++++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 config/experiments/crystals/penguin.yaml diff --git a/config/experiments/crystals/penguin.yaml b/config/experiments/crystals/penguin.yaml new file mode 100644 index 000000000..fb1eedaec --- /dev/null +++ b/config/experiments/crystals/penguin.yaml @@ -0,0 +1,103 @@ +# @package _global_ + +defaults: + - override /env: crystals/ccrystal + - override /gflownet: trajectorybalance + - override /proxy: crystals/dave + - override /logger: wandb + +device: cpu + +# Environment +env: + composition_kwargs: + elements: [1, 3, 6, 7, 8, 9, 12, 14, 15, 16, 17, 26] + max_atoms: 50 + max_atom_i: 16 + space_group_kwargs: + space_groups_subset: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 25, 26, 29, 30, 31, 33, 36, 38, 40, 41, 43, 44, 46, 47, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 82, 84, 85, 86, 87, 88, 92, 99, 102, 107, 113, 114, 121, 122, 123, 126, 129, 131, 136, 137, 138, 139, 140, 141, 146, 147, 148, 150, 155, 156, 160, 161, 162, 163, 164, 166, 167, 176, 181, 185, 186, 187, 189, 192, 194, 198, 199, 205, 206, 216, 217, 220, 221, 224, 225, 227, 229, 230] + lattice_parameters_kwargs: + min_length: 0.9 + max_length: 100.0 + min_angle: 50.0 + max_angle: 150.0 + n_comp: 5 + beta_params_min: 0.1 + beta_params_max: 100.0 + min_incr: 0.1 + fixed_distr_params: + beta_weights: 1.0 + beta_alpha: 10.0 + beta_beta: 10.0 + bernoulli_eos_prob: 0.1 + bernoulli_bts_prob: 0.1 + random_distr_params: + beta_weights: 1.0 + beta_alpha: 10.0 + beta_beta: 10.0 + bernoulli_eos_prob: 0.1 + bernoulli_bts_prob: 0.1 + reward_func: boltzmann + reward_beta: 1 + buffer: + replay_capacity: 0 + test: + type: pkl + path: /home/mila/h/hernanga/gflownet/data/crystals/matbench_normed_l0.9-100_a50-150_val_12_SGinter_states_energy.pkl + output_csv: ccrystal_val.csv + output_pkl: ccrystal_val.pkl + +# GFlowNet hyperparameters +gflownet: + random_action_prob: 0.1 + optimizer: + batch_size: + forward: 10 + backward_replay: -1 + lr: 0.0001 + z_dim: 16 + lr_z_mult: 100 + n_train_steps: 25000 + lr_decay_period: 1000000 + replay_sampling: weighted + +# Policy +policy: + forward: + type: mlp + n_hid: 256 + n_layers: 3 + checkpoint: forward + backward: + type: mlp + n_hid: 256 + n_layers: 3 + shared_weights: False + checkpoint: backward + +# WandB +logger: + lightweight: True + project_name: "crystal-gfn" + tags: + - gflownet + - crystals + - matbench + - workshop23 + checkpoints: + period: 500 + do: + online: true + test: + n_trajs_logprobs: 10 + period: 500 + n: 10 + n_top_k: 5000 + top_k: 100 + top_k_period: -1 + +# Hydra +hydra: + run: + dir: ${user.logdir.root}/workshop23/discrete-matbench/${oc.env:SLURM_JOB_ID,local}/${now:%Y-%m-%d_%H-%M-%S} + From 4743023a3fe02f72a205369dd4e16c088c5efa90 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 29 Sep 2023 10:40:34 -0400 Subject: [PATCH 156/205] n_max in test data --- gflownet/utils/buffer.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/gflownet/utils/buffer.py b/gflownet/utils/buffer.py index b5d3d3e42..eb105613a 100644 --- a/gflownet/utils/buffer.py +++ b/gflownet/utils/buffer.py @@ -217,6 +217,9 @@ def make_data_set(self, config): f", but only {n_samples_new} are valid according to the " "environment settings. Invalid samples have been discarded." ) + n_max = 100 + samples = samples[:n_max] + print(f"Only the first {n_max} samples will be kept in the data.") print("Remember to write a function to normalise the data in code") print("Max number of elements in data set has to match config") print("Actually, write a function that contrasts the stats") From 5a09200d3c9970ddf7fb9e2a274568b95dd5caa5 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 29 Sep 2023 10:40:54 -0400 Subject: [PATCH 157/205] update eval to recent changes --- scripts/eval_gflownet.py | 73 +++++++++++++++++++++++++++++++++++----- 1 file changed, 64 insertions(+), 9 deletions(-) diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index c0cb359db..e081804c7 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -1,6 +1,7 @@ """ Computes evaluation metrics and plots from a pre-trained GFlowNet model. """ +import pickle import sys from argparse import ArgumentParser from pathlib import Path @@ -8,10 +9,12 @@ import hydra import torch from hydra import compose, initialize, initialize_config_dir +import pandas as pd from omegaconf import OmegaConf from torch.distributions.categorical import Categorical -from gflownet.gflownet import GFlowNetAgent, Policy +from gflownet.gflownet import GFlowNetAgent +from gflownet.utils.policy import parse_policy_config def add_args(parser): @@ -68,31 +71,55 @@ def main(args): device=config.device, float_precision=config.float_precision, ) + forward_config = parse_policy_config(config, kind="forward") + backward_config = parse_policy_config(config, kind="backward") + forward_policy = hydra.utils.instantiate( + forward_config, + env=env, + device=config.device, + float_precision=config.float_precision, + ) + backward_policy = hydra.utils.instantiate( + backward_config, + env=env, + device=config.device, + float_precision=config.float_precision, + base=forward_policy, + ) gflownet = hydra.utils.instantiate( config.gflownet, device=config.device, float_precision=config.float_precision, env=env, buffer=config.env.buffer, + forward_policy=forward_policy, + backward_policy=backward_policy, logger=logger, ) # Load final models - ckpt = Path(args.run_path) / config.logger.logdir.ckpts - forward_final = [ - f for f in ckpt.glob(f"{config.gflownet.policy.forward.checkpoint}*final*") + ckpt = [ + f for f in Path(args.run_path).rglob(config.logger.logdir.ckpts) if f.is_dir() ][0] + forward_final = [f for f in ckpt.glob(f"*final*")][0] + backward_final = [f for f in ckpt.glob(f"*final*")][0] gflownet.forward_policy.model.load_state_dict( torch.load(forward_final, map_location=set_device(args.device)) ) - backward_final = [ - f for f in ckpt.glob(f"{config.gflownet.policy.backward.checkpoint}*final*") - ][0] gflownet.backward_policy.model.load_state_dict( torch.load(backward_final, map_location=set_device(args.device)) ) # Test GFlowNet model gflownet.logger.test.n = args.n_samples - l1, kl, jsd, figs = gflownet.test() + ( + l1, + kl, + jsd, + corr_prob_traj_rew, + var_logrew_logp, + nll, + figs, + env_metrics, + ) = gflownet.test() # Save figures keys = ["True reward and GFlowNet samples", "GFlowNet KDE Policy", "Reward KDE"] fignames = ["samples", "kde_gfn", "kde_reward"] @@ -100,7 +127,35 @@ def main(args): output_dir.mkdir(parents=True, exist_ok=True) for fig, figname in zip(figs, fignames): output_fig = output_dir / figname - fig.savefig(output_fig, bbox_inches="tight") + if fig is not None: + fig.savefig(output_fig, bbox_inches="tight") + + # Print metrics + print(f"L1: {l1}") + print(f"KL: {kl}") + print(f"JSD: {jsd}") + print(f"Corr (exp(logp), rewards): {corr_prob_traj_rew}") + print(f"Var (log(R) - logp): {var_logrew_logp}") + print(f"NLL: {nll}") + + # Sample from trained GFlowNet + output_dir = Path(args.run_path) / "eval/samples" + output_dir.mkdir(parents=True, exist_ok=True) + if args.n_samples > 0 and args.n_samples <= 1e5: + print(f"Sampling {args.n_samples} forward trajectories from GFlowNet...") + batch, times = gflownet.sample_batch(n_forward=args.n_samples, train=False) + x_sampled = batch.get_terminating_states(proxy=True) + energies = env.oracle(x_sampled) + x_sampled = batch.get_terminating_states() + df = pd.DataFrame( + { + "readable": [env.state2readable(x) for x in x_sampled], + "energies": energies.tolist(), + } + ) + df.to_csv(output_dir / "gfn_samples.csv") + dct = {"x": x_sampled, "energy": energies} + pickle.dump(dct, open(output_dir / "gfn_samples.pkl", "wb")) if __name__ == "__main__": From eab43f999f3764c9fc798e3f31dca50b9fe0de1c Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Fri, 29 Sep 2023 11:39:41 -0400 Subject: [PATCH 158/205] add missing params --- config/env/crystals/crystal.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/config/env/crystals/crystal.yaml b/config/env/crystals/crystal.yaml index 8a844ea17..3bb3640ef 100644 --- a/config/env/crystals/crystal.yaml +++ b/config/env/crystals/crystal.yaml @@ -7,6 +7,8 @@ _target_: gflownet.envs.crystals.crystal.Crystal id: crystal composition_kwargs: elements: 89 + max_atoms: 20 + max_atom_i: 16 lattice_parameters_kwargs: min_length: 1.0 max_length: 5.0 From 5c0e8b9bc9cb3c1626fdaa1d99c80671c5d57b19 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 29 Sep 2023 13:33:23 -0400 Subject: [PATCH 159/205] Add sampling from untrained GFN --- scripts/eval_gflownet.py | 44 ++++++++++++++++++++++++++++------------ 1 file changed, 31 insertions(+), 13 deletions(-) diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index e081804c7..b1b34ce27 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -36,6 +36,11 @@ def add_args(parser): type=int, help="Number of sequences to sample", ) + parser.add_argument( + "--random", + action="store_true", + help="Sample from an untrained GFlowNet", + ) parser.add_argument("--device", default="cpu", type=str) return parser @@ -97,17 +102,20 @@ def main(args): logger=logger, ) # Load final models - ckpt = [ - f for f in Path(args.run_path).rglob(config.logger.logdir.ckpts) if f.is_dir() - ][0] - forward_final = [f for f in ckpt.glob(f"*final*")][0] - backward_final = [f for f in ckpt.glob(f"*final*")][0] - gflownet.forward_policy.model.load_state_dict( - torch.load(forward_final, map_location=set_device(args.device)) - ) - gflownet.backward_policy.model.load_state_dict( - torch.load(backward_final, map_location=set_device(args.device)) - ) + if not args.random: + ckpt = [ + f + for f in Path(args.run_path).rglob(config.logger.logdir.ckpts) + if f.is_dir() + ][0] + forward_final = [f for f in ckpt.glob(f"*final*")][0] + backward_final = [f for f in ckpt.glob(f"*final*")][0] + gflownet.forward_policy.model.load_state_dict( + torch.load(forward_final, map_location=set_device(args.device)) + ) + gflownet.backward_policy.model.load_state_dict( + torch.load(backward_final, map_location=set_device(args.device)) + ) # Test GFlowNet model gflownet.logger.test.n = args.n_samples ( @@ -153,9 +161,19 @@ def main(args): "energies": energies.tolist(), } ) - df.to_csv(output_dir / "gfn_samples.csv") + if args.random: + df.to_csv(output_dir / "random_samples.csv") + else: + df.to_csv(output_dir / "gfn_samples.csv") dct = {"x": x_sampled, "energy": energies} - pickle.dump(dct, open(output_dir / "gfn_samples.pkl", "wb")) + if args.random: + pickle.dump(dct, open(output_dir / "random_samples.pkl", "wb")) + else: + pickle.dump(dct, open(output_dir / "gfn_samples.pkl", "wb")) + + # Store test data set + gflownet.buffer.test.rename(columns={"samples": "readable"}) + gflownet.buffer.test.to_csv(output_dir / "test_samples.csv") if __name__ == "__main__": From d40f09736be285ac6cbaf04353d2230439a09e0b Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Fri, 29 Sep 2023 14:34:50 -0400 Subject: [PATCH 160/205] update `load_gflow_net_from_run_path` --- gflownet/utils/common.py | 63 ++++++++++++++++++++++++++++------------ 1 file changed, 44 insertions(+), 19 deletions(-) diff --git a/gflownet/utils/common.py b/gflownet/utils/common.py index afa751816..c7a78f597 100644 --- a/gflownet/utils/common.py +++ b/gflownet/utils/common.py @@ -11,6 +11,8 @@ from omegaconf import OmegaConf from torchtyping import TensorType +from gflownet.utils.policy import parse_policy_config + def set_device(device: Union[str, torch.device]): if isinstance(device, torch.device): @@ -102,58 +104,81 @@ def find_latest_checkpoint(ckpt_dir, pattern): return sorted(ckpts, key=lambda f: float(f.stem.split("iter")[1]))[-1] -def load_gflow_net_from_run_path(run_path, device="cuda"): - device = str(device) +def load_gflow_net_from_run_path( + run_path, no_wandb=True, print_config=False, device="cuda" +): run_path = resolve_path(run_path) hydra_dir = run_path / ".hydra" + with initialize_config_dir( version_base=None, config_dir=str(hydra_dir), job_name="xxx" ): config = compose(config_name="config") + + if print_config: print(OmegaConf.to_yaml(config)) - # Disable wandb - config.logger.do.online = False + + if no_wandb: + # Disable wandb + config.logger.do.online = False + # Logger logger = instantiate(config.logger, config, _recursive_=False) # The proxy is required in the env for scoring: might be an oracle or a model proxy = instantiate( config.proxy, - device=device, + device=config.device, float_precision=config.float_precision, ) # The proxy is passed to env and used for computing rewards env = instantiate( config.env, proxy=proxy, - device=device, + device=config.device, + float_precision=config.float_precision, + ) + forward_config = parse_policy_config(config, kind="forward") + backward_config = parse_policy_config(config, kind="backward") + forward_policy = instantiate( + forward_config, + env=env, + device=config.device, float_precision=config.float_precision, ) + backward_policy = instantiate( + backward_config, + env=env, + device=config.device, + float_precision=config.float_precision, + base=forward_policy, + ) gflownet = instantiate( config.gflownet, - device=device, + device=config.device, float_precision=config.float_precision, env=env, buffer=config.env.buffer, + forward_policy=forward_policy, + backward_policy=backward_policy, logger=logger, ) - # Load final models - ckpt_dir = Path(run_path) / config.logger.logdir.ckpts - forward_latest = find_latest_checkpoint( - ckpt_dir, config.gflownet.policy.forward.checkpoint - ) + + # ------------------------------- + # ----- Load final models ----- + # ------------------------------- + + ckpt = [f for f in run_path.rglob(config.logger.logdir.ckpts) if f.is_dir()][0] + forward_final = find_latest_checkpoint(ckpt, "pf") gflownet.forward_policy.model.load_state_dict( - torch.load(forward_latest, map_location=device) + torch.load(forward_final, map_location=set_device(device)) ) try: - backward_latest = find_latest_checkpoint( - ckpt_dir, config.gflownet.policy.backward.checkpoint - ) + backward_final = find_latest_checkpoint(ckpt, "pb") gflownet.backward_policy.model.load_state_dict( - torch.load(backward_latest, map_location=device) + torch.load(backward_final, map_location=set_device(device)) ) - except AttributeError: + except ValueError: print("No backward policy found") - return gflownet From d8f7711da2bdff50b0dd8f3a494172680a601831 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Fri, 29 Sep 2023 14:35:09 -0400 Subject: [PATCH 161/205] switch `tqdm` / `enumerate` --- gflownet/gflownet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/gflownet.py b/gflownet/gflownet.py index 9b6f8f14f..163c4e681 100644 --- a/gflownet/gflownet.py +++ b/gflownet/gflownet.py @@ -737,7 +737,7 @@ def estimate_logprobs_data( # Create an environment for each data point and trajectory and set the state envs = [] mult_indices = max(n_states, n_trajectories) - for state_idx, x in tqdm(enumerate(states_term)): + for state_idx, x in enumerate(tqdm(states_term)): for traj_idx in range(n_trajectories): idx = int(mult_indices * state_idx + traj_idx) env = self.env.copy().reset(idx) From bdd1088f079f5b5bff46d790bf1e793b865b6a37 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Fri, 29 Sep 2023 14:36:14 -0400 Subject: [PATCH 162/205] add new args --- scripts/eval_gflownet.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index e081804c7..145b9e631 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -36,6 +36,31 @@ def add_args(parser): type=int, help="Number of sequences to sample", ) + parser.add_argument( + "--sampling_batch_size", + default=100, + type=int, + help="Number of samples to generate at a time to " + + "avoid memory issues. Will sum to n_samples.", + ) + parser.add_argument( + "--output_dir", + default=None, + type=str, + help="Path to output directory. If not provided, will use run_path.", + ) + parser.add_argument( + "--print_config", + default=False, + action="store_true", + help="Print the config file", + ) + parser.add_argument( + "--samples_only", + default=False, + action="store_true", + help="Only sample from the model, do not compute metrics", + ) parser.add_argument("--device", default="cpu", type=str) return parser From 1e225cedf099ffa843a3683bdff45082614f6e5f Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Fri, 29 Sep 2023 14:36:39 -0400 Subject: [PATCH 163/205] New `get_batch_sizes` `print_args` --- scripts/eval_gflownet.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index 145b9e631..029665316 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -65,6 +65,38 @@ def add_args(parser): return parser +def get_batch_sizes(total, b=1): + """ + Batches an iterable into chunks of size n and returns their expected lengths + + Args: + total (int): total samples to produce + b (int): the batch size + + Returns: + list: list of batch sizes + """ + n = total // b + chunks = [b] * n + if total % b != 0: + chunks += [total % b] + return chunks + + +def print_args(args): + """ + Prints the arguments + + Args: + args (argparse.Namespace): the parsed arguments + """ + print("Arguments:") + darg = vars(args) + max_k = max([len(k) for k in darg]) + for k in darg: + print(f"\t{k:{max_k}}: {darg[k]}") + + def set_device(device: str): if device.lower() == "cuda" and torch.cuda.is_available(): return torch.device("cuda") From ec7a898723d6b3ee5c13121e2485a48caedf3c38 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Fri, 29 Sep 2023 14:37:13 -0400 Subject: [PATCH 164/205] `base_dir` and condiftional `test()` --- scripts/eval_gflownet.py | 128 ++++++++++++--------------------------- 1 file changed, 39 insertions(+), 89 deletions(-) diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index 029665316..e28d19e6b 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -105,95 +105,45 @@ def set_device(device: str): def main(args): - # Load config - with initialize_config_dir( - version_base=None, config_dir=args.run_path + "/.hydra", job_name="xxx" - ): - config = compose(config_name="config") - print(OmegaConf.to_yaml(config)) - # Disable wandb - config.logger.do.online = False - # Logger - logger = hydra.utils.instantiate(config.logger, config, _recursive_=False) - # The proxy is required in the env for scoring: might be an oracle or a model - proxy = hydra.utils.instantiate( - config.proxy, - device=config.device, - float_precision=config.float_precision, - ) - # The proxy is passed to env and used for computing rewards - env = hydra.utils.instantiate( - config.env, - proxy=proxy, - device=config.device, - float_precision=config.float_precision, - ) - forward_config = parse_policy_config(config, kind="forward") - backward_config = parse_policy_config(config, kind="backward") - forward_policy = hydra.utils.instantiate( - forward_config, - env=env, - device=config.device, - float_precision=config.float_precision, - ) - backward_policy = hydra.utils.instantiate( - backward_config, - env=env, - device=config.device, - float_precision=config.float_precision, - base=forward_policy, - ) - gflownet = hydra.utils.instantiate( - config.gflownet, - device=config.device, - float_precision=config.float_precision, - env=env, - buffer=config.env.buffer, - forward_policy=forward_policy, - backward_policy=backward_policy, - logger=logger, - ) - # Load final models - ckpt = [ - f for f in Path(args.run_path).rglob(config.logger.logdir.ckpts) if f.is_dir() - ][0] - forward_final = [f for f in ckpt.glob(f"*final*")][0] - backward_final = [f for f in ckpt.glob(f"*final*")][0] - gflownet.forward_policy.model.load_state_dict( - torch.load(forward_final, map_location=set_device(args.device)) - ) - gflownet.backward_policy.model.load_state_dict( - torch.load(backward_final, map_location=set_device(args.device)) - ) - # Test GFlowNet model - gflownet.logger.test.n = args.n_samples - ( - l1, - kl, - jsd, - corr_prob_traj_rew, - var_logrew_logp, - nll, - figs, - env_metrics, - ) = gflownet.test() - # Save figures - keys = ["True reward and GFlowNet samples", "GFlowNet KDE Policy", "Reward KDE"] - fignames = ["samples", "kde_gfn", "kde_reward"] - output_dir = Path(args.run_path) / "figures" - output_dir.mkdir(parents=True, exist_ok=True) - for fig, figname in zip(figs, fignames): - output_fig = output_dir / figname - if fig is not None: - fig.savefig(output_fig, bbox_inches="tight") - - # Print metrics - print(f"L1: {l1}") - print(f"KL: {kl}") - print(f"JSD: {jsd}") - print(f"Corr (exp(logp), rewards): {corr_prob_traj_rew}") - print(f"Var (log(R) - logp): {var_logrew_logp}") - print(f"NLL: {nll}") + base_dir = Path(args.output_dir or args.run_path) + + # --------------------------------- + # ----- Test GFlowNet model ----- + # --------------------------------- + + if not args.samples_only: + gflownet.logger.test.n = args.n_samples + ( + l1, + kl, + jsd, + corr_prob_traj_rew, + var_logrew_logp, + nll, + figs, + env_metrics, + ) = gflownet.test() + # Save figures + keys = ["True reward and GFlowNet samples", "GFlowNet KDE Policy", "Reward KDE"] + fignames = ["samples", "kde_gfn", "kde_reward"] + + output_dir = base_dir / "figures" + print("output_dir: ", str(output_dir)) + output_dir.mkdir(parents=True, exist_ok=True) + + for fig, figname in zip(figs, fignames): + output_fig = output_dir / figname + if fig is not None: + fig.savefig(output_fig, bbox_inches="tight") + print(f"Saved figures to {output_dir}") + + # Print metrics + print(f"L1: {l1}") + print(f"KL: {kl}") + print(f"JSD: {jsd}") + print(f"Corr (exp(logp), rewards): {corr_prob_traj_rew}") + print(f"Var (log(R) - logp): {var_logrew_logp}") + print(f"NLL: {nll}") # Sample from trained GFlowNet output_dir = Path(args.run_path) / "eval/samples" From a94d4a232de2b930f327150d53dff1d8384239be Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Fri, 29 Sep 2023 15:23:49 -0400 Subject: [PATCH 165/205] use `load_gflow_net_from_run_path` --- scripts/eval_gflownet.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index e28d19e6b..fada4b096 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -2,19 +2,18 @@ Computes evaluation metrics and plots from a pre-trained GFlowNet model. """ import pickle +import shutil import sys from argparse import ArgumentParser from pathlib import Path -import hydra -import torch -from hydra import compose, initialize, initialize_config_dir import pandas as pd -from omegaconf import OmegaConf -from torch.distributions.categorical import Categorical +import torch +from tqdm import tqdm + +sys.path.append(str(Path(__file__).resolve().parent.parent)) -from gflownet.gflownet import GFlowNetAgent -from gflownet.utils.policy import parse_policy_config +from gflownet.utils.common import load_gflow_net_from_run_path def add_args(parser): @@ -105,6 +104,14 @@ def set_device(device: str): def main(args): + gflownet = load_gflow_net_from_run_path( + run_path=args.run_path, + device=args.device, + no_wandb=True, + print_config=args.print_config, + ) + env = gflownet.env + base_dir = Path(args.output_dir or args.run_path) # --------------------------------- From 45660025790971eb1323fd2ccafc14ff7ccf98de Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Fri, 29 Sep 2023 15:24:29 -0400 Subject: [PATCH 166/205] batched sampling --- scripts/eval_gflownet.py | 51 ++++++++++++++++++++++++++++++---------- 1 file changed, 38 insertions(+), 13 deletions(-) diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index fada4b096..b6febf048 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -152,25 +152,50 @@ def main(args): print(f"Var (log(R) - logp): {var_logrew_logp}") print(f"NLL: {nll}") - # Sample from trained GFlowNet - output_dir = Path(args.run_path) / "eval/samples" + # ------------------------------------------ + # ----- Sample from trained GFlowNet ----- + # ------------------------------------------ + + output_dir = base_dir / "eval" / "samples" output_dir.mkdir(parents=True, exist_ok=True) + tmp_dir = output_dir / "tmp" + tmp_dir.mkdir(parents=True, exist_ok=True) + if args.n_samples > 0 and args.n_samples <= 1e5: - print(f"Sampling {args.n_samples} forward trajectories from GFlowNet...") - batch, times = gflownet.sample_batch(n_forward=args.n_samples, train=False) - x_sampled = batch.get_terminating_states(proxy=True) - energies = env.oracle(x_sampled) - x_sampled = batch.get_terminating_states() - df = pd.DataFrame( - { - "readable": [env.state2readable(x) for x in x_sampled], - "energies": energies.tolist(), - } + print( + f"Sampling {args.n_samples} forward trajectories", + f"from GFlowNet in batches of {args.sampling_batch_size}", ) + for i, bs in enumerate( + tqdm(get_batch_sizes(args.n_samples, args.sampling_batch_size)) + ): + batch, times = gflownet.sample_batch(n_forward=bs, train=False) + x_sampled = batch.get_terminating_states(proxy=True) + energies = env.oracle(x_sampled) + x_sampled = batch.get_terminating_states() + df = pd.DataFrame( + { + "readable": [env.state2readable(x) for x in x_sampled], + "energies": energies.tolist(), + } + ) + df.to_csv(tmp_dir / f"gfn_samples_{i}.csv") + dct = {"x": x_sampled, "energy": energies} + pickle.dump(dct, open(tmp_dir / f"gfn_samples_{i}.pkl", "wb")) + + # Concatenate all samples + print("Concatenating sample CSVs") + df = pd.concat([pd.read_csv(f) for f in tqdm(list(tmp_dir.glob("*.csv")))]) df.to_csv(output_dir / "gfn_samples.csv") - dct = {"x": x_sampled, "energy": energies} + dct = {} + for f in tqdm(list(tmp_dir.glob("*.pkl"))): + tmp_dict = pickle.load(open(f, "rb")) + dct = {k: v + tmp_dict[k] for k, v in dct.items()} pickle.dump(dct, open(output_dir / "gfn_samples.pkl", "wb")) + if "y" in input("Delete temporary files? (y/n)"): + shutil.rmtree(tmp_dir) + if __name__ == "__main__": parser = ArgumentParser() From f13a5fc0bbe4b456f997b22422bdc36bf085d3c6 Mon Sep 17 00:00:00 2001 From: Victor Schmidt Date: Fri, 29 Sep 2023 15:24:45 -0400 Subject: [PATCH 167/205] no grad & print --- scripts/eval_gflownet.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index b6febf048..482a93039 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -202,6 +202,8 @@ def main(args): _, override_args = parser.parse_known_args() parser = add_args(parser) args = parser.parse_args() + torch.set_grad_enabled(False) torch.set_num_threads(1) + print_args(args) main(args) sys.exit() From 4eefd384bdba095588ac71e2b8b78f8382b98bd8 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 29 Sep 2023 17:56:55 -0400 Subject: [PATCH 168/205] sample random crystals --- scripts/eval_gflownet.py | 47 +++++++++++++++++++++++++++++++++++----- 1 file changed, 41 insertions(+), 6 deletions(-) diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index b1b34ce27..36ab0db8b 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -15,6 +15,7 @@ from gflownet.gflownet import GFlowNetAgent from gflownet.utils.policy import parse_policy_config +from crystalrandom import generate_random_crystals def add_args(parser): @@ -37,10 +38,15 @@ def add_args(parser): help="Number of sequences to sample", ) parser.add_argument( - "--random", + "--randominit", action="store_true", help="Sample from an untrained GFlowNet", ) + parser.add_argument( + "--random_crystals", + action="store_true", + help="Sample crystals uniformly, without constraints", + ) parser.add_argument("--device", default="cpu", type=str) return parser @@ -101,8 +107,37 @@ def main(args): backward_policy=backward_policy, logger=logger, ) + # Sample random crystals uniformly without constraints + output_dir = Path(args.run_path) / "eval/samples" + output_dir.mkdir(parents=True, exist_ok=True) + if args.random_crystals and args.n_samples > 0 and args.n_samples <= 1e5: + print(f"Sampling {args.n_samples} random crystals without constraints...") + x_sampled = generate_random_crystals( + n_samples=args.n_samples, + elements=config.env.composition_kwargs.elements, + min_elements=2, + max_elements=5, + max_atoms=config.env.composition_kwargs.max_atoms, + max_atom_i=config.env.composition_kwargs.max_atom_i, + space_groups=config.env.space_group_kwargs.space_groups_subset, + min_length=config.env.lattice_parameters_kwargs.min_length, + max_length=config.env.lattice_parameters_kwargs.max_length, + min_angle=config.env.lattice_parameters_kwargs.min_angle, + max_angle=config.env.lattice_parameters_kwargs.max_angle, + ) + energies = env.oracle(env.statebatch2proxy(x_sampled)) + df = pd.DataFrame( + { + "readable": [env.state2readable(x) for x in x_sampled], + "energies": energies.tolist(), + } + ) + df.to_csv(output_dir / "randomcrystals_samples.csv") + dct = {"x": x_sampled, "energy": energies} + pickle.dump(dct, open(output_dir / "randomcrystals_samples.pkl", "wb")) + # Load final models - if not args.random: + if not args.randominit: ckpt = [ f for f in Path(args.run_path).rglob(config.logger.logdir.ckpts) @@ -161,13 +196,13 @@ def main(args): "energies": energies.tolist(), } ) - if args.random: - df.to_csv(output_dir / "random_samples.csv") + if args.randominit: + df.to_csv(output_dir / "randominit_samples.csv") else: df.to_csv(output_dir / "gfn_samples.csv") dct = {"x": x_sampled, "energy": energies} - if args.random: - pickle.dump(dct, open(output_dir / "random_samples.pkl", "wb")) + if args.randominit: + pickle.dump(dct, open(output_dir / "randominit_samples.pkl", "wb")) else: pickle.dump(dct, open(output_dir / "gfn_samples.pkl", "wb")) From 76ec4d51e52942f7d3a92de2d0552cb9b49cb190 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 29 Sep 2023 21:11:02 -0400 Subject: [PATCH 169/205] Rename variable and add option to control whether sg to lp constraints are applied --- gflownet/envs/crystals/ccrystal.py | 24 +++++++++++++++--------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 80dd423d3..19830339f 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -74,13 +74,15 @@ def __init__( composition_kwargs: Optional[Dict] = None, space_group_kwargs: Optional[Dict] = None, lattice_parameters_kwargs: Optional[Dict] = None, - do_stoichiometry_sg_check: bool = False, + do_composition_to_sg_constraints: bool = True, + do_sg_to_lp_constraints: bool = True, **kwargs, ): self.composition_kwargs = composition_kwargs or {} self.space_group_kwargs = space_group_kwargs or {} self.lattice_parameters_kwargs = lattice_parameters_kwargs or {} - self.do_stoichiometry_sg_check = do_stoichiometry_sg_check + self.do_composition_to_sg_constraints = do_composition_to_sg_constraints + self.do_sg_to_lp_constraints = do_sg_to_lp_constraints composition = Composition(**self.composition_kwargs) space_group = SpaceGroup(**self.space_group_kwargs) @@ -521,15 +523,16 @@ def step( if action_subenv == self.subenvs[stage].eos: stage = Stage.next(stage) if stage == Stage.SPACE_GROUP: - if self.do_stoichiometry_sg_check: + if self.do_composition_to_sg_constraints: self.subenvs[Stage.SPACE_GROUP].set_n_atoms_compatibility_dict( self.subenvs[Stage.COMPOSITION].state ) elif stage == Stage.LATTICE_PARAMETERS: - lattice_system = self.subenvs[Stage.SPACE_GROUP].lattice_system - self.subenvs[Stage.LATTICE_PARAMETERS].set_lattice_system( - lattice_system - ) + if self.do_sg_to_lp_constraints: + lattice_system = self.subenvs[Stage.SPACE_GROUP].lattice_system + self.subenvs[Stage.LATTICE_PARAMETERS].set_lattice_system( + lattice_system + ) elif stage == Stage.DONE: self.n_actions += 1 self.done = True @@ -860,14 +863,17 @@ def set_state(self, state: List, done: Optional[bool] = False): """ if self.subenvs[Stage.SPACE_GROUP].done: lattice_system = self.subenvs[Stage.SPACE_GROUP].lattice_system - if lattice_system != "None": + if lattice_system != "None" and self.do_sg_to_lp_constraints: self.subenvs[Stage.LATTICE_PARAMETERS].set_lattice_system( lattice_system ) else: self.subenvs[Stage.LATTICE_PARAMETERS].set_lattice_system(TRICLINIC) # Set stoichiometry constraints in space group sub-environment - if self.do_stoichiometry_sg_check and self.subenvs[Stage.COMPOSITION].done: + if ( + self.do_composition_to_sg_constraints + and self.subenvs[Stage.COMPOSITION].done + ): self.subenvs[Stage.SPACE_GROUP].set_n_atoms_compatibility_dict( self.subenvs[Stage.COMPOSITION].state ) From 013ccdce865c2503987e35e0e2407ea3e11f5e46 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 29 Sep 2023 21:14:52 -0400 Subject: [PATCH 170/205] black and isort --- gflownet/utils/common.py | 6 +++++- scripts/eval_gflownet.py | 5 +++-- scripts/fit_lattice_proxy.py | 1 - scripts/mp20_matbench_lp_range.py | 1 - 4 files changed, 8 insertions(+), 5 deletions(-) diff --git a/gflownet/utils/common.py b/gflownet/utils/common.py index 3185acaf7..2e4a6b2d2 100644 --- a/gflownet/utils/common.py +++ b/gflownet/utils/common.py @@ -105,7 +105,11 @@ def find_latest_checkpoint(ckpt_dir, pattern): def load_gflow_net_from_run_path( - run_path, no_wandb=True, print_config=False, device="cuda", load_final_ckpt=True, + run_path, + no_wandb=True, + print_config=False, + device="cuda", + load_final_ckpt=True, ): run_path = resolve_path(run_path) hydra_dir = run_path / ".hydra" diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index 5613a8386..9cb8f7535 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -13,10 +13,11 @@ sys.path.append(str(Path(__file__).resolve().parent.parent)) -from gflownet.gflownet import GFlowNetAgent -from gflownet.utils.policy import parse_policy_config from crystalrandom import generate_random_crystals + +from gflownet.gflownet import GFlowNetAgent from gflownet.utils.common import load_gflow_net_from_run_path +from gflownet.utils.policy import parse_policy_config def add_args(parser): diff --git a/scripts/fit_lattice_proxy.py b/scripts/fit_lattice_proxy.py index a416a9bc8..83550d8b2 100644 --- a/scripts/fit_lattice_proxy.py +++ b/scripts/fit_lattice_proxy.py @@ -17,7 +17,6 @@ from gflownet.envs.crystals.lattice_parameters import LatticeParameters from gflownet.proxy.crystals.lattice_parameters import PICKLE_PATH - DATASET_PATH = ( Path(__file__).parents[1] / "data" / "crystals" / "matbench_mp_e_form_lp_stats.csv" ) diff --git a/scripts/mp20_matbench_lp_range.py b/scripts/mp20_matbench_lp_range.py index 4d3ec5180..8ae7fdedd 100644 --- a/scripts/mp20_matbench_lp_range.py +++ b/scripts/mp20_matbench_lp_range.py @@ -7,7 +7,6 @@ import numpy as np import pandas as pd - if __name__ == "__main__": mp = pd.read_csv(Path(__file__).parents[1] / "data/crystals/mp20_lp_stats.csv") mb = pd.read_csv( From 651baecb793b3be5bb03b3a624d56bd9468a0828 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 30 Sep 2023 09:18:10 -0400 Subject: [PATCH 171/205] update variables in ccrystal config --- config/env/crystals/ccrystal.yaml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/config/env/crystals/ccrystal.yaml b/config/env/crystals/ccrystal.yaml index ca3c7b288..47fd9455d 100644 --- a/config/env/crystals/ccrystal.yaml +++ b/config/env/crystals/ccrystal.yaml @@ -17,10 +17,11 @@ lattice_parameters_kwargs: space_group_kwargs: space_groups_subset: null # Stoichiometry <-> space group check -do_stoichiometry_sg_check: True +do_composition_to_sg_constraints: True +self.do_sg_to_lp_constraints: True # Buffer buffer: data_path: null train: null - test: nulll + test: null From 9d2d9848738fa26d84d6fae355240489187d0cf6 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 30 Sep 2023 12:15:11 -0400 Subject: [PATCH 172/205] Cherry pick batch logprobs and fix conflicts --- config/logger/base.yaml | 1 + gflownet/gflownet.py | 108 ++++++++++++++++++++-------------------- 2 files changed, 55 insertions(+), 54 deletions(-) diff --git a/config/logger/base.yaml b/config/logger/base.yaml index 640167c81..7707b75c5 100644 --- a/config/logger/base.yaml +++ b/config/logger/base.yaml @@ -22,6 +22,7 @@ test: top_k_period: -1 # Number of backward trajectories to estimate the log likelihood of each test data point n_trajs_logprobs: 10 + logprobs_batch_size: 100 # Maximum number of test data points to compute log likelihood probs. max_data_logprobs: 1e5 # Oracle metrics diff --git a/gflownet/gflownet.py b/gflownet/gflownet.py index 163c4e681..aa46f2322 100644 --- a/gflownet/gflownet.py +++ b/gflownet/gflownet.py @@ -669,6 +669,7 @@ def estimate_logprobs_data( n_trajectories: int = 1, max_iters_per_traj: int = 10, max_data_size: int = 1e5, + batch_size: int = 100, ): """ Estimates the probability of sampling with current GFlowNet policy @@ -717,7 +718,6 @@ def estimate_logprobs_data( each data point. """ print("Compute logprobs...", flush=True) - batch = Batch(env=self.env, device=self.device, float_type=self.float) times = {} # Determine terminating states if isinstance(data, list): @@ -734,43 +734,7 @@ def estimate_logprobs_data( assert ( n_states < max_data_size ), "The size of the test data is larger than max_data_size ({max_data_size})." - # Create an environment for each data point and trajectory and set the state - envs = [] - mult_indices = max(n_states, n_trajectories) - for state_idx, x in enumerate(tqdm(states_term)): - for traj_idx in range(n_trajectories): - idx = int(mult_indices * state_idx + traj_idx) - env = self.env.copy().reset(idx) - env.set_state(x, done=True) - envs.append(env) - # Sample trajectories - max_iters = n_trajectories * max_iters_per_traj - print( - "Sampling backward actions from test data to estimate logprobs...", - flush=True, - ) - while envs: - # Sample backward actions - actions = self.sample_actions( - envs, - batch, - backward=True, - no_random=True, - times=times, - ) - # Update environments with sampled actions - envs, actions, valids = self.step(envs, actions, backward=True) - # Add to batch - batch.add_to_batch(envs, actions, valids, backward=True, train=True) - # Filter out finished trajectories - envs = [env for env in envs if not env.equal(env.state, env.source)] - # Prepare data structures to compute log probabilities - print("Done sampling backwards", flush=True) - traj_indices_batch = tlong( - batch.get_unique_trajectory_indices(), device=self.device - ) - data_indices = traj_indices_batch // mult_indices - traj_indices = traj_indices_batch % mult_indices + # Compute log probabilities in batches logprobs_f = torch.full( (n_states, n_trajectories), -torch.inf, @@ -783,23 +747,58 @@ def estimate_logprobs_data( dtype=self.float, device=self.device, ) - # Compute log probabilities of the trajectories - logprobs_f[data_indices, traj_indices] = self.compute_logprobs_trajectories( - batch, backward=False - ) - logprobs_b[data_indices, traj_indices] = self.compute_logprobs_trajectories( - batch, backward=True - ) - # Check whether logprobs are finite - all_logprobs_finite = torch.all( - torch.logical_and(torch.isfinite(logprobs_f), torch.isfinite(logprobs_b)), - dim=1, + mult_indices = max(n_states, n_trajectories) + init_batch = 0 + end_batch = min(batch_size, n_states) + print( + "Sampling backward actions from test data to estimate logprobs...", + flush=True, ) - if not torch.all(all_logprobs_finite): - print("The following samples have yielded inf or nan logprobs:") - for state, is_finite in zip(states_term, all_logprobs_finite): - if not is_finite: - print(self.env.state2readable(state)) + while init_batch < n_states: + batch = Batch(env=self.env, device=self.device, float_type=self.float) + # Create an environment for each data point and trajectory and set the state + envs = [] + for state_idx in range(init_batch, end_batch): + for traj_idx in range(n_trajectories): + idx = int(mult_indices * state_idx + traj_idx) + env = self.env.copy().reset(idx) + env.set_state(states_term[state_idx], done=True) + envs.append(env) + # Sample trajectories + max_iters = n_trajectories * max_iters_per_traj + while envs: + # Sample backward actions + actions = self.sample_actions( + envs, + batch, + backward=True, + no_random=True, + times=times, + ) + # Update environments with sampled actions + envs, actions, valids = self.step(envs, actions, backward=True) + assert all(valids) + # Add to batch + batch.add_to_batch(envs, actions, valids, backward=True, train=True) + # Filter out finished trajectories + envs = [env for env in envs if not env.equal(env.state, env.source)] + # Prepare data structures to compute log probabilities + traj_indices_batch = tlong( + batch.get_unique_trajectory_indices(), device=self.device + ) + data_indices = traj_indices_batch // mult_indices + traj_indices = traj_indices_batch % mult_indices + # Compute log probabilities of the trajectories + logprobs_f[data_indices, traj_indices] = self.compute_logprobs_trajectories( + batch, backward=False + ) + logprobs_b[data_indices, traj_indices] = self.compute_logprobs_trajectories( + batch, backward=True + ) + # Increment batch indices + init_batch += batch_size + end_batch = min(end_batch + batch_size, n_states) + # Compute log of the average probabilities of the ratio PF / PB logprobs_estimates = torch.logsumexp( logprobs_f - logprobs_b, dim=1 @@ -995,6 +994,7 @@ def test(self, **plot_kwargs): x_tt, n_trajectories=self.logger.test.n_trajs_logprobs, max_data_size=self.logger.test.max_data_logprobs, + batch_size=self.logger.test.logprobs_batch_size, ) rewards_x_tt = self.env.reward_batch(x_tt) corr_prob_traj_rewards = np.corrcoef( From 811d9a02815f39b7b7009a33f558bf8268af44e7 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Sat, 30 Sep 2023 12:39:08 -0400 Subject: [PATCH 173/205] Cherry pick pbar in logprobs --- gflownet/gflownet.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gflownet/gflownet.py b/gflownet/gflownet.py index aa46f2322..7cd8e96fb 100644 --- a/gflownet/gflownet.py +++ b/gflownet/gflownet.py @@ -754,6 +754,7 @@ def estimate_logprobs_data( "Sampling backward actions from test data to estimate logprobs...", flush=True, ) + pbar = tqdm(total=n_states) while init_batch < n_states: batch = Batch(env=self.env, device=self.device, float_type=self.float) # Create an environment for each data point and trajectory and set the state @@ -798,6 +799,7 @@ def estimate_logprobs_data( # Increment batch indices init_batch += batch_size end_batch = min(end_batch + batch_size, n_states) + pbar.update(end_batch - init_batch) # Compute log of the average probabilities of the ratio PF / PB logprobs_estimates = torch.logsumexp( From 79eb6a7e40695199398dd9482dc34af21815718c Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 16 Oct 2023 08:23:16 -0400 Subject: [PATCH 174/205] Functionality to clip reward of dave proxy --- gflownet/proxy/crystals/dave.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/gflownet/proxy/crystals/dave.py b/gflownet/proxy/crystals/dave.py index e560d3143..7197a9513 100644 --- a/gflownet/proxy/crystals/dave.py +++ b/gflownet/proxy/crystals/dave.py @@ -45,6 +45,10 @@ def __init__(self, ckpt_path=None, release=None, rescale_outputs=True, **kwargs) super().__init__(**kwargs) self.rescale_outputs = rescale_outputs self.scaled = False + if "clip" in kwargs: + self.clip = kwargs["clip"] + else: + self.clip = False print("Initializing DAVE proxy:") print(" Checking out release:", release) @@ -145,6 +149,21 @@ def __call__(self, states: TensorType["batch", "102"]) -> TensorType["batch"]: if self.rescale_outputs: y = y * self.scales["y"]["std"] + self.scales["y"]["mean"] + if self.clip and self.clip.do: + if self.rescale_outputs: + if self.clip.min_stds: + y_min = -1.0 * self.clip.min_stds * self.scales["y"]["std"] + else: + y_min = None + if self.clip.max_stds: + y_max = self.clip.max_stds * self.scales["y"]["std"] + else: + y_max = None + else: + y_min = self.clip.min + y_max = self.clip.max + y = torch.clamp(min=y_min, max=y_max) + return y @torch.no_grad() From 9abee106503269c7a9fe57b889bddff5445e8dd6 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 16 Oct 2023 08:24:26 -0400 Subject: [PATCH 175/205] Config dave proxy: add clip variables --- config/proxy/crystals/dave.yaml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/config/proxy/crystals/dave.yaml b/config/proxy/crystals/dave.yaml index 06c4eb2a2..d61c938fa 100644 --- a/config/proxy/crystals/dave.yaml +++ b/config/proxy/crystals/dave.yaml @@ -5,3 +5,9 @@ ckpt_path: mila: /network/scratch/s/schmidtv/crystals-proxys/proxy-ckpts/ victor: ~/Documents/Github/ActiveLearningMaterials/checkpoints/980065c0/checkpoints-3ff648a2 rescale_outputs: true +clip: + do: False + min_stds: null + max_stds: null + min: null + max: null From f4464ae1fc143d811588ce75fc1b96d045847436 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 16 Oct 2023 08:26:22 -0400 Subject: [PATCH 176/205] min/max length/angle in [0,1] --- scripts/eval_gflownet.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/scripts/eval_gflownet.py b/scripts/eval_gflownet.py index 9cb8f7535..760155811 100644 --- a/scripts/eval_gflownet.py +++ b/scripts/eval_gflownet.py @@ -233,10 +233,10 @@ def main(args): max_atoms=config.env.composition_kwargs.max_atoms, max_atom_i=config.env.composition_kwargs.max_atom_i, space_groups=config.env.space_group_kwargs.space_groups_subset, - min_length=config.env.lattice_parameters_kwargs.min_length, - max_length=config.env.lattice_parameters_kwargs.max_length, - min_angle=config.env.lattice_parameters_kwargs.min_angle, - max_angle=config.env.lattice_parameters_kwargs.max_angle, + min_length=0.0, + max_length=1.0, + min_angle=0.0, + max_angle=1.0, ) energies = env.oracle(env.statebatch2proxy(x_sampled)) df = pd.DataFrame( From 75413bbabb199b2799a4c4a7af919e284c909b2b Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Fri, 20 Oct 2023 18:16:14 -0400 Subject: [PATCH 177/205] Add --time to launch.py arguments --- LAUNCH.md | 8 ++++++-- mila/launch.py | 8 ++++++++ mila/sbatch/template-conda.sh | 3 ++- mila/sbatch/template-venv.sh | 3 ++- 4 files changed, 18 insertions(+), 4 deletions(-) diff --git a/LAUNCH.md b/LAUNCH.md index aaadd7c2f..2d4b2358f 100644 --- a/LAUNCH.md +++ b/LAUNCH.md @@ -7,12 +7,12 @@ In the following, `$root` refers to the root of the current repository. ```sh usage: launch.py [-h] [--help-md] [--job_name JOB_NAME] [--outdir OUTDIR] [--cpus_per_task CPUS_PER_TASK] [--mem MEM] [--gres GRES] - [--partition PARTITION] [--modules MODULES] + [--partition PARTITION] [--time TIME] [--modules MODULES] [--conda_env CONDA_ENV] [--venv VENV] [--template TEMPLATE] [--code_dir CODE_DIR] [--git_checkout GIT_CHECKOUT] [--jobs JOBS] [--dry-run] [--verbose] [--force] -optional arguments: +options: -h, --help show this help message and exit --help-md Show an extended help message as markdown. Can be useful to overwrite LAUNCH.md with `$ python @@ -26,6 +26,9 @@ optional arguments: --gres GRES gres per node (e.g. gpu:1). Defaults to gpu:1 --partition PARTITION slurm partition to use for the job. Defaults to long + --time TIME wall clock time limit (e.g. 2-12:00:00). See: + https://slurm.schedmd.com/sbatch.html#OPT_time + Defaults to None --modules MODULES string after 'module load'. Defaults to anaconda/3 cuda/11.3 --conda_env CONDA_ENV @@ -69,6 +72,7 @@ modules : anaconda/3 cuda/11.3 outdir : $SCRATCH/gflownet/logs/slurm partition : long template : $root/mila/sbatch/template-conda.sh +time : None venv : None verbose : False ``` diff --git a/mila/launch.py b/mila/launch.py index dce36db8d..5e2f5379f 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -406,6 +406,7 @@ def code_dir_for_slurm_tmp_dir_checkout(git_checkout): "outdir": "$SCRATCH/gflownet/logs/slurm", "partition": "long", "template": "$root/mila/sbatch/template-conda.sh", + "time": None, "venv": None, "verbose": False, } @@ -454,6 +455,13 @@ def code_dir_for_slurm_tmp_dir_checkout(git_checkout): help="slurm partition to use for the job." + f" Defaults to {defaults['partition']}", ) + parser.add_argument( + "--time", + type=str, + help="wall clock time limit (e.g. 2-12:00:00). " + + "See: https://slurm.schedmd.com/sbatch.html#OPT_time" + + f" Defaults to {defaults['time']}", + ) parser.add_argument( "--modules", type=str, diff --git a/mila/sbatch/template-conda.sh b/mila/sbatch/template-conda.sh index f2ff3de33..983728b78 100644 --- a/mila/sbatch/template-conda.sh +++ b/mila/sbatch/template-conda.sh @@ -5,10 +5,11 @@ #SBATCH --mem={mem} #SBATCH --gres={gres} #SBATCH --partition={partition} +#SBATCH --time={time} module load {modules} conda activate {conda_env} cd {code_dir} -python main.py {main_args} \ No newline at end of file +python main.py {main_args} diff --git a/mila/sbatch/template-venv.sh b/mila/sbatch/template-venv.sh index d719ce58d..66cf0d694 100644 --- a/mila/sbatch/template-venv.sh +++ b/mila/sbatch/template-venv.sh @@ -5,10 +5,11 @@ #SBATCH --mem={mem} #SBATCH --gres={gres} #SBATCH --partition={partition} +#SBATCH --time={time} module load {modules} source {venv}/bin/activate cd {code_dir} -python main.py {main_args} \ No newline at end of file +python main.py {main_args} From 60f5dcccecc1e66f1269b98016e2b5b3a9ed015d Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 23 Oct 2023 16:00:43 -0400 Subject: [PATCH 178/205] Fix env fixtures in test_ccrystal.py --- tests/gflownet/envs/test_ccrystal.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 7c8bd2e26..9a84efcd2 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -38,6 +38,7 @@ def env(): return CCrystal( composition_kwargs={"elements": 4}, + do_composition_to_sg_constraints=False, space_group_kwargs={"space_groups_subset": list(range(1, 15 + 1)) + [105]}, ) @@ -46,7 +47,7 @@ def env(): def env_with_stoichiometry_sg_check(): return CCrystal( composition_kwargs={"elements": 4}, - do_stoichiometry_sg_check=True, + do_composition_to_sg_constraints=True, space_group_kwargs={"space_groups_subset": SG_SUBSET_ALL_CLS_PS}, ) @@ -341,7 +342,7 @@ def test__state_of_subenv__returns_expected( ), ( "env_with_stoichiometry_sg_check", - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [2, 4, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [True, True, False], True, True, From cf7cc448c8f719a808785fc0c95ad0084627360a Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 23 Oct 2023 16:01:30 -0400 Subject: [PATCH 179/205] Fix test of comp-to-sg constraints --- tests/gflownet/envs/test_ccrystal.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 9a84efcd2..6bd49c576 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -382,10 +382,11 @@ def test__set_state__sets_state_subenvs_dones_and_constraints( # Check composition constraints if has_composition_constraints: + n_atoms = [n for n in env.subenvs[Stage.COMPOSITION].state if n > 0] n_atoms_compatibility_dict = env.subenvs[ Stage.SPACE_GROUP ].build_n_atoms_compatibility_dict( - env.subenvs[Stage.COMPOSITION].state, + n_atoms, env.subenvs[Stage.SPACE_GROUP].space_groups.keys(), ) assert ( From c5106eb50e8ef53bd60cc532b909dceeeb5ad6b6 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 23 Oct 2023 19:05:23 -0400 Subject: [PATCH 180/205] Ensure right dtype in composition/statetorch2oracle --- gflownet/envs/crystals/composition.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/composition.py b/gflownet/envs/crystals/composition.py index 9da55b481..b00b1da06 100644 --- a/gflownet/envs/crystals/composition.py +++ b/gflownet/envs/crystals/composition.py @@ -445,12 +445,14 @@ def statetorch2oracle( ---- oracle_states : Tensor """ + states_float = states.to(self.float) + states_oracle = torch.zeros( (states.shape[0], N_ELEMENTS_ORACLE + 1), device=self.device, dtype=self.float, ) - states_oracle[:, tlong(self.elements, device=self.device)] = states + states_oracle[:, tlong(self.elements, device=self.device)] = states_float return states_oracle def statebatch2oracle( From 80a32cf5e54b4c72893ea32b97ac5ae73980dc1f Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 23 Oct 2023 21:21:47 -0400 Subject: [PATCH 181/205] Fix composition tests --- gflownet/envs/crystals/composition.py | 4 ++-- tests/gflownet/envs/test_composition.py | 30 ++++++++++++++++++++++--- 2 files changed, 29 insertions(+), 5 deletions(-) diff --git a/gflownet/envs/crystals/composition.py b/gflownet/envs/crystals/composition.py index b00b1da06..55b8bb186 100644 --- a/gflownet/envs/crystals/composition.py +++ b/gflownet/envs/crystals/composition.py @@ -425,8 +425,8 @@ def state2oracle(self, state: List = None) -> Tensor: if state is None: state = self.state return self.statetorch2oracle( - torch.unsqueeze(tfloat(states, device=self.device), 0) - ) + torch.unsqueeze(tfloat(state, device=self.device, float_type=self.float), 0) + )[0] def statetorch2oracle( self, states: TensorType["batch", "state_dim"] diff --git a/tests/gflownet/envs/test_composition.py b/tests/gflownet/envs/test_composition.py index 888e7221a..31fcd470e 100644 --- a/tests/gflownet/envs/test_composition.py +++ b/tests/gflownet/envs/test_composition.py @@ -38,15 +38,39 @@ def test__environment__initializes_properly(elements): [ ( [0, 0, 2, 0], - [0, 0, 2, 0], + [ + # fmt: off + 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + # fmt: on + ], ), ( [3, 0, 0, 0], - [3, 0, 0, 0], + [ + # fmt: off + 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + # fmt: on + ], ), ( [0, 1, 0, 1], - [0, 1, 0, 1], + [ + # fmt: off + 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + # fmt: on + ], ), ], ) From 229bed5114e73a9dea2a23f51ba26f5db5f6d39a Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 23 Oct 2023 21:22:06 -0400 Subject: [PATCH 182/205] Fix crystal tests --- tests/gflownet/envs/test_crystal.py | 116 ++++++++++++++++++++++++++-- 1 file changed, 110 insertions(+), 6 deletions(-) diff --git a/tests/gflownet/envs/test_crystal.py b/tests/gflownet/envs/test_crystal.py index c11cac1ec..5d4c9cbed 100644 --- a/tests/gflownet/envs/test_crystal.py +++ b/tests/gflownet/envs/test_crystal.py @@ -66,11 +66,47 @@ def test__pad_depad_action(env): [ [ (2, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 4, 5, 6), - Tensor([1.0, 1.0, 1.0, 1.0, 3.0, 1.4, 1.8, 2.2, 78.0, 90.0, 102.0]), + Tensor( + [ + # fmt: off + # Composition state + 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + # Spacegroup state + 3.0, + # Lattice parameter state + 1.4, 1.8, 2.2, 78.0, 90.0, 102.0, + # fmt: on + ] + ), ], [ (2, 4, 9, 0, 3, 0, 0, 105, 5, 3, 1, 0, 0, 9), - Tensor([4.0, 9.0, 0.0, 3.0, 105.0, 3.0, 2.2, 1.4, 30.0, 30.0, 138.0]), + Tensor( + [ + # fmt: off + # Composition state + 0.0, 4.0, 9.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + # Spacegroup state + 105.0, + # Lattice parameter state + 3.0, 2.2, 1.4, 30.0, 30.0, 138.0, + # fmt: on + ] + ), ], ], ) @@ -83,11 +119,47 @@ def test__state2oracle__returns_expected_value(env, state, expected): [ [ (2, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 4, 5, 6), - Tensor([1.0, 1.0, 1.0, 1.0, 3.0, 1.4, 1.8, 2.2, 78.0, 90.0, 102.0]), + Tensor( + [ + # fmt: off + # Composition state + 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + # Spacegroup state + 3.0, + # Lattice parameter state + 1.4, 1.8, 2.2, 78.0, 90.0, 102.0, + # fmt: on + ] + ), ], [ (2, 4, 9, 0, 3, 0, 0, 105, 5, 3, 1, 0, 0, 9), - Tensor([4.0, 9.0, 0.0, 3.0, 105.0, 3.0, 2.2, 1.4, 30.0, 30.0, 138.0]), + Tensor( + [ + # fmt: off + # Composition state + 0.0, 4.0, 9.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + # Spacegroup state + 105.0, + # Lattice parameter state + 3.0, 2.2, 1.4, 30.0, 30.0, 138.0, + # fmt: on + ] + ), ], ], ) @@ -105,8 +177,40 @@ def test__state2proxy__returns_expected_value(env, state, expected): ], Tensor( [ - [1.0, 1.0, 1.0, 1.0, 3.0, 1.4, 1.8, 2.2, 78.0, 90.0, 102.0], - [4.0, 9.0, 0.0, 3.0, 105.0, 3.0, 2.2, 1.4, 30.0, 30.0, 138.0], + [ + # fmt: off + # Composition state + 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + # Spacegroup state + 3.0, + # Lattice parameter state + 1.4, 1.8, 2.2, 78.0, 90.0, 102.0, + # fmt: on + ], + [ + # fmt: off + # Composition state + 0.0, 4.0, 9.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + # Spacegroup state + 105.0, + # Lattice parameter state + 3.0, 2.2, 1.4, 30.0, 30.0, 138.0, + # fmt: on + ], ] ), ], From 3e92da9f9477d5f122c81493571d10c97f544e34 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Thu, 19 Oct 2023 10:11:56 -0400 Subject: [PATCH 183/205] Add function to get spacegroup index --- gflownet/envs/crystals/spacegroup.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/gflownet/envs/crystals/spacegroup.py b/gflownet/envs/crystals/spacegroup.py index 7a935db56..29f04e6d4 100644 --- a/gflownet/envs/crystals/spacegroup.py +++ b/gflownet/envs/crystals/spacegroup.py @@ -546,6 +546,21 @@ def get_space_group_symbol(self, state: List[int] = None) -> str: def space_group_symbol(self) -> str: return self.get_space_group_symbol(self.state) + def get_space_group(self, state: List[int] = None) -> int: + """ + Returns the index of the space group symbol given a state. + """ + if state is None: + state = self.state + if state[self.sg_idx] != 0: + return state[self.sg_idx] + else: + return None + + @property + def space_group(self) -> int: + return self.get_space_group(self.state) + # TODO: Technically the crystal class could be determined from crystal-lattice # system + point symmetry def get_crystal_class(self, state: List[int] = None) -> str: From 0039bd024454df836bf056bee929a1c19bc8f5bc Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 23 Oct 2023 12:59:06 -0400 Subject: [PATCH 184/205] Add hyperparam to choos SG first in ccrystal --- gflownet/envs/crystals/ccrystal.py | 183 +++++++++++++++++++++++------ 1 file changed, 146 insertions(+), 37 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 19830339f..bbf3d5251 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -27,22 +27,6 @@ class Stage(Enum): LATTICE_PARAMETERS = 2 DONE = 3 - def next(self) -> "Stage": - """ - Returns the next Stage in the enumeration or None if at the last stage. - """ - if self.value + 1 == len(Stage): - return None - return Stage(self.value + 1) - - def prev(self) -> "Stage": - """ - Returns the previous Stage in the enumeration or DONE if from the first stage. - """ - if self.value - 1 < 0: - return Stage.DONE - return Stage(self.value - 1) - def to_pad(self) -> int: """ Maps stage value to a padding. The following mapping is used: @@ -75,14 +59,22 @@ def __init__( space_group_kwargs: Optional[Dict] = None, lattice_parameters_kwargs: Optional[Dict] = None, do_composition_to_sg_constraints: bool = True, + do_sg_to_composition_constraints: bool = True, do_sg_to_lp_constraints: bool = True, + do_sg_before_composition: bool = False, **kwargs, ): - self.composition_kwargs = composition_kwargs or {} + do_composition_sg_checks = ( + do_sg_to_composition_constraints and do_sg_before_composition + ) + self.composition_kwargs = dict( + composition_kwargs or {}, do_spacegroup_check=do_composition_sg_checks + ) self.space_group_kwargs = space_group_kwargs or {} self.lattice_parameters_kwargs = lattice_parameters_kwargs or {} self.do_composition_to_sg_constraints = do_composition_to_sg_constraints self.do_sg_to_lp_constraints = do_sg_to_lp_constraints + self.do_sg_before_composition = do_sg_before_composition composition = Composition(**self.composition_kwargs) space_group = SpaceGroup(**self.space_group_kwargs) @@ -102,7 +94,8 @@ def __init__( # 0-th element of state encodes current stage: 0 for composition, # 1 for space group, 2 for lattice parameters - self.source = [Stage.COMPOSITION.value] + initial_stage = self._get_next_stage(None) + self.source = [initial_stage.value] for subenv in self.subenvs.values(): self.source.extend(subenv.source) @@ -378,6 +371,86 @@ def _get_states_of_subenv( return states[:, init_col:end_col] init_col = end_col + def _is_source_state(self, state) -> bool: + """Determines if the provided state is a source state. + This method returns True if the provided state corresponds to the initial state + of any of the sub-environments. Returns False otherwise. + """ + stage = self._get_stage(state) + return self._get_state_of_subenv(state, stage) == self.subenvs[stage].source + + def _get_previous_stage(self, stage: Stage) -> Stage: + """Return the stage that preceeds the provided stage. + There are two possible stage ordering depending on + self.do_sg_before_composition. Either : + Composition -> SpaceGroup -> LatticeParameter -> Done + or + SpaceGroup -> Composition -> LatticeParameter -> Done + """ + if self.do_sg_before_composition: + if stage is Stage.SPACE_GROUP: + # Space group is the initial stage. No previous stage. + return Stage.DONE + elif stage is Stage.COMPOSITION: + return Stage.SPACE_GROUP + elif stage is Stage.LATTICE_PARAMETERS: + return Stage.COMPOSITION + elif stage is Stage.DONE: + return Stage.LATTICE_PARAMETERS + else: + raise ValueError(f"Unrecognized stage {stage}.") + + else: + if stage is Stage.COMPOSITION: + # Space group is the initial stage. No previous stage. + return Stage.DONE + elif stage is Stage.SPACE_GROUP: + return Stage.COMPOSITION + elif stage is Stage.LATTICE_PARAMETERS: + return Stage.SPACE_GROUP + elif stage is Stage.DONE: + return Stage.LATTICE_PARAMETERS + else: + raise ValueError(f"Unrecognized stage {stage}.") + + def _get_next_stage(self, stage: Stage = None) -> Stage: + """Returns the stage that follows the provided stage. + If no stage is provided, this function will return the initial stage. There are + two possible stage ordering depending on self.do_sg_before_composition. Either : + Composition -> SpaceGroup -> LatticeParameter -> Done + or + SpaceGroup -> Composition -> LatticeParameter -> Done + """ + if self.do_sg_before_composition: + if stage is None: + # In the event of a environment reset, return the initial stage + return Stage.SPACE_GROUP + elif stage is Stage.SPACE_GROUP: + return Stage.COMPOSITION + elif stage is Stage.COMPOSITION: + return Stage.LATTICE_PARAMETERS + elif stage is Stage.LATTICE_PARAMETERS: + return Stage.DONE + elif stage is Stage.DONE: + return None + else: + raise ValueError(f"Unrecognized stage {stage}.") + + else: + if stage is None: + # In the event of a environment reset, return the initial stage + return Stage.COMPOSITION + elif stage is Stage.COMPOSITION: + return Stage.SPACE_GROUP + elif stage is Stage.SPACE_GROUP: + return Stage.LATTICE_PARAMETERS + elif stage is Stage.LATTICE_PARAMETERS: + return Stage.DONE + elif stage is Stage.DONE: + return None + else: + raise ValueError(f"Unrecognized stage {stage}.") + # TODO: set mask of done state if stage is not the current one for correctness. def get_mask_invalid_actions_forward( self, state: Optional[List[int]] = None, done: Optional[bool] = None @@ -521,22 +594,34 @@ def step( # If action is EOS of subenv, advance stage and set constraints or exit if action_subenv == self.subenvs[stage].eos: - stage = Stage.next(stage) - if stage == Stage.SPACE_GROUP: - if self.do_composition_to_sg_constraints: + stage = self._get_next_stage(stage) + + if stage is Stage.SPACE_GROUP: + if ( + not self.do_sg_before_composition + and self.do_composition_to_sg_constraints + ): self.subenvs[Stage.SPACE_GROUP].set_n_atoms_compatibility_dict( self.subenvs[Stage.COMPOSITION].state ) - elif stage == Stage.LATTICE_PARAMETERS: + + elif stage is Stage.COMPOSITION: + if self.do_sg_before_composition and self.do_stoichiometry_sg_check: + space_group = self.subenvs[Stage.SPACE_GROUP].space_group + self.subenvs[Stage.COMPOSITION].space_group = space_group + + elif stage is Stage.LATTICE_PARAMETERS: if self.do_sg_to_lp_constraints: lattice_system = self.subenvs[Stage.SPACE_GROUP].lattice_system self.subenvs[Stage.LATTICE_PARAMETERS].set_lattice_system( lattice_system ) - elif stage == Stage.DONE: + + elif stage is Stage.DONE: self.n_actions += 1 self.done = True return self.state, self.eos, True + else: raise ValueError(f"Unrecognized stage {stage}.") @@ -585,9 +670,10 @@ def step_backwards( # If state of subenv is source of subenv, decrease stage if self._get_state_of_subenv(self.state, stage) == self.subenvs[stage].source: - stage = Stage.prev(stage) - # If stage is DONE, set global source and return - if stage == Stage.DONE: + stage = self._get_previous_stage(stage) + # If stage is DONE, we've returned to the environment's initial state, + # set global source and return + if stage is Stage.DONE: self.state = self.source return self.state, action, True @@ -647,7 +733,7 @@ def sample_actions_batch( and stage != Stage(0) and state_subenv == self.subenvs[stage].source ): - stage = Stage.prev(stage) + stage = self._get_previous_stage(stage) states_dict[stage].append(state_subenv) stages.append(stage) stages_tensor = tlong([stage.value for stage in stages], device=self.device) @@ -726,7 +812,7 @@ def get_logprobs( and stage != Stage(0) and state_subenv == self.subenvs[stage].source ): - stage = Stage.prev(stage) + stage = self._get_previous_stage(stage) states_dict[stage].append(state_subenv) stages.append(stage) stages_tensor = tlong([stage.value for stage in stages], device=self.device) @@ -845,23 +931,40 @@ def statetorch2proxy( def set_state(self, state: List, done: Optional[bool] = False): super().set_state(state, done) + stage = self._get_stage(state) stage_idx = self._get_stage(state).value # Determine which subenvs are done based on stage and done - done_subenvs = [True] * stage_idx + [False] * (len(self.subenvs) - stage_idx) + done_subenvs = { + Stage.COMPOSITION: False, + Stage.SPACE_GROUP: False, + Stage.LATTICE_PARAMETERS: False, + } + if stage is Stage.COMPOSITION and self.do_sg_before_composition: + done_subenvs[Stage.SPACE_GROUP] = True + elif stage is Stage.SPACE_GROUP and not self.do_sg_before_composition: + done_subenvs[Stage.COMPOSITION] = True + elif stage is Stage.LATTICE_PARAMETERS: + done_subenvs[Stage.COMPOSITION] = True + done_subenvs[Stage.SPACE_GROUP] = True + elif stage is Stage.DONE: + for subenv in done_subenvs: + done_subenvs[subenv] = True done_subenvs[-1] = done + # Set state and done of each sub-environment for (stage, subenv), subenv_done in zip(self.subenvs.items(), done_subenvs): - subenv.set_state(self._get_state_of_subenv(state, stage), subenv_done) + stage_done = done_subenvs[stage] + subenv.set_state(self._get_state_of_subenv(state, stage), stage_done) - """ - We synchronize LatticeParameter's lattice system with the one of SpaceGroup - (if it was set) or reset it to the default triclinic otherwise. Why this is - needed: for backward sampling, where we start from an arbitrary terminal state, - and need to synchronize the LatticeParameter's lattice system to what that - state indicates, - """ if self.subenvs[Stage.SPACE_GROUP].done: + """ + We synchronize LatticeParameter's lattice system with the one of SpaceGroup + (if it was set) or reset it to the default triclinic otherwise. Why this is + needed: for backward sampling, where we start from an arbitrary terminal + state and need to synchronize the LatticeParameter's lattice system to what + that state indicates, + """ lattice_system = self.subenvs[Stage.SPACE_GROUP].lattice_system if lattice_system != "None" and self.do_sg_to_lp_constraints: self.subenvs[Stage.LATTICE_PARAMETERS].set_lattice_system( @@ -869,6 +972,12 @@ def set_state(self, state: List, done: Optional[bool] = False): ) else: self.subenvs[Stage.LATTICE_PARAMETERS].set_lattice_system(TRICLINIC) + + # Set the stoichiometry constraints in the composition sub-environment + if self.do_sg_before_composition and self.do_sg_to_composition_constraints: + space_group = self.subenvs[Stage.SPACE_GROUP].space_group + self.subenvs[Stage.COMPOSITION].space_group = space_group + # Set stoichiometry constraints in space group sub-environment if ( self.do_composition_to_sg_constraints From 4944f364f6e8dbeb1207c110a58712e09008eaf7 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 23 Oct 2023 13:04:43 -0400 Subject: [PATCH 185/205] Add tests for sg-first ccrystal environment --- tests/gflownet/envs/test_ccrystal.py | 385 ++++++++++++++++++++++++--- 1 file changed, 348 insertions(+), 37 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 6bd49c576..b14242f3c 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -52,29 +52,55 @@ def env_with_stoichiometry_sg_check(): ) -def test__stage_next__returns_expected(): - assert Stage.next(Stage.COMPOSITION) == Stage.SPACE_GROUP - assert Stage.next(Stage.SPACE_GROUP) == Stage.LATTICE_PARAMETERS - assert Stage.next(Stage.LATTICE_PARAMETERS) == Stage.DONE - assert Stage.next(Stage.DONE) == None +@pytest.fixture +def env_sg_first(): + return CCrystal( + composition_kwargs={"elements": 4}, + do_sg_to_composition_constraints=True, + do_sg_before_composition=True, + ) -def test__stage_prev__returns_expected(): - assert Stage.prev(Stage.COMPOSITION) == Stage.DONE - assert Stage.prev(Stage.SPACE_GROUP) == Stage.COMPOSITION - assert Stage.prev(Stage.LATTICE_PARAMETERS) == Stage.SPACE_GROUP - assert Stage.prev(Stage.DONE) == Stage.LATTICE_PARAMETERS +def test__stage_next__returns_expected(env, env_sg_first): + assert env._get_next_stage(None) == Stage.COMPOSITION + assert env._get_next_stage(Stage.COMPOSITION) == Stage.SPACE_GROUP + assert env._get_next_stage(Stage.SPACE_GROUP) == Stage.LATTICE_PARAMETERS + assert env._get_next_stage(Stage.LATTICE_PARAMETERS) == Stage.DONE + assert env._get_next_stage(Stage.DONE) == None + + assert env_sg_first._get_next_stage(None) == Stage.SPACE_GROUP + assert env_sg_first._get_next_stage(Stage.SPACE_GROUP) == Stage.COMPOSITION + assert env_sg_first._get_next_stage(Stage.COMPOSITION) == Stage.LATTICE_PARAMETERS + assert env_sg_first._get_next_stage(Stage.LATTICE_PARAMETERS) == Stage.DONE + assert env_sg_first._get_next_stage(Stage.DONE) == None + + +def test__stage_prev__returns_expected(env, env_sg_first): + assert env._get_previous_stage(Stage.COMPOSITION) == Stage.DONE + assert env._get_previous_stage(Stage.SPACE_GROUP) == Stage.COMPOSITION + assert env._get_previous_stage(Stage.LATTICE_PARAMETERS) == Stage.SPACE_GROUP + assert env._get_previous_stage(Stage.DONE) == Stage.LATTICE_PARAMETERS + + assert env_sg_first._get_previous_stage(Stage.COMPOSITION) == Stage.DONE + assert env_sg_first._get_previous_stage(Stage.SPACE_GROUP) == Stage.COMPOSITION + assert ( + env_sg_first._get_previous_stage(Stage.LATTICE_PARAMETERS) == Stage.SPACE_GROUP + ) + assert env_sg_first._get_previous_stage(Stage.DONE) == Stage.LATTICE_PARAMETERS def test__environment__initializes_properly(env): pass -def test__environment__has_expected_initial_state(env): +@pytest.mark.parametrize("env_input, initial_stage", [["env", 0], ["env_sg_first", 1]]) +def test__environment__has_expected_initial_state(env_input, initial_stage, request): """ The source of the composition and space group environments is all 0s. The source of the continuous lattice parameters environment is all -1s. """ + env = request.getfixturevalue(env_input) + expected_initial_state = [initial_stage] + [0] * (4 + 3 + 6) assert ( env.state == env.source == [0] * (1 + 4 + 3) + [-1] * 6 ) # stage + n elements + space groups + lattice parameters @@ -310,7 +336,7 @@ def test__state_of_subenv__returns_expected( @pytest.mark.parametrize( - "env_input, state, dones, has_lattice_parameters, has_composition_constraints", + "env_input, state, dones, has_lattice_parameters, has_composition_constraints, has_spacegroup_constraints", [ ( "env", @@ -318,6 +344,7 @@ def test__state_of_subenv__returns_expected( [False, False, False], False, False, + False, ), ( "env", @@ -325,6 +352,7 @@ def test__state_of_subenv__returns_expected( [False, False, False], False, False, + False, ), ( "env", @@ -332,6 +360,7 @@ def test__state_of_subenv__returns_expected( [True, False, False], True, False, + False, ), ( "env", @@ -339,6 +368,7 @@ def test__state_of_subenv__returns_expected( [True, True, False], True, False, + False, ), ( "env_with_stoichiometry_sg_check", @@ -346,6 +376,39 @@ def test__state_of_subenv__returns_expected( [True, True, False], True, True, + False, + ), + ( + "env_sg_first", + [1, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + [False, False, False], + False, + False, + False, + ), + ( + "env_sg_first", + [1, 0, 0, 0, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [False, False, False], + False, + False, + False, + ), + ( + "env_sg_first", + [0, 3, 1, 0, 6, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [False, True, False], + True, + False, + True, + ), + ( + "env_sg_first", + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + [True, True, False], + True, + False, + True, ), ], ) @@ -355,6 +418,7 @@ def test__set_state__sets_state_subenvs_dones_and_constraints( dones, has_lattice_parameters, has_composition_constraints, + has_spacegroup_constraints, request, ): env = request.getfixturevalue(env_input) @@ -394,24 +458,50 @@ def test__set_state__sets_state_subenvs_dones_and_constraints( == env.subenvs[Stage.SPACE_GROUP].n_atoms_compatibility_dict ) + # Check spacegroup constraints + if has_spacegroup_constraints: + assert ( + env.subenvs[Stage.COMPOSITION].space_group + == env.subenvs[Stage.SPACE_GROUP].space_group + ) + @pytest.mark.parametrize( - "state", + "env_input, state", [ - [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1], - [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1], - [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1], - [1, 3, 1, 0, 6, 1, 2, 2, -1, -1, -1, -1, -1, -1], - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], - [2, 1, 0, 4, 0, 4, 3, 105, 0.12, 0.23, 0.34, 0.45, 0.56, 0.67], - [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + ("env", [0, 0, 4, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1]), + ("env", [0, 3, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1]), + ("env", [0, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1]), + ("env", [0, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1]), + ("env", [1, 3, 1, 0, 6, 1, 0, 0, -1, -1, -1, -1, -1, -1]), + ("env", [1, 3, 1, 0, 6, 1, 1, 0, -1, -1, -1, -1, -1, -1]), + ("env", [1, 3, 1, 0, 6, 1, 2, 0, -1, -1, -1, -1, -1, -1]), + ("env", [1, 3, 1, 0, 6, 1, 2, 2, -1, -1, -1, -1, -1, -1]), + ("env", [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]), + ("env", [2, 1, 0, 4, 0, 4, 3, 105, 0.12, 0.23, 0.34, 0.45, 0.56, 0.67]), + ("env", [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71]), + ("env_sg_first", [1, 0, 0, 0, 0, 1, 0, 0, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [1, 0, 0, 0, 0, 1, 1, 0, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [1, 0, 0, 0, 0, 1, 2, 0, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [1, 0, 0, 0, 0, 1, 2, 2, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [0, 3, 0, 0, 0, 1, 2, 2, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [0, 3, 0, 0, 6, 1, 2, 2, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [0, 3, 1, 0, 6, 1, 2, 2, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]), + ( + "env_sg_first", + [2, 1, 0, 4, 0, 4, 3, 105, 0.12, 0.23, 0.34, 0.45, 0.56, 0.67], + ), + ( + "env_sg_first", + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.75, 0.74, 0.73, 0.72, 0.71], + ), ], ) -def test__get_mask_invalid_actions_backward__returns_expected_general_case(env, state): +def test__get_mask_invalid_actions_backward__returns_expected_general_case( + env_input, state, request +): + env = request.getfixturevalue(env_input) stage = env._get_stage(state) mask = env.get_mask_invalid_actions_backward(state, done=False) for stg, subenv in env.subenvs.items(): @@ -430,22 +520,28 @@ def test__get_mask_invalid_actions_backward__returns_expected_general_case(env, @pytest.mark.parametrize( - "state", + "env_input, state", [ - [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [1, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1], - [2, 3, 1, 0, 6, 1, 2, 2, -1, -1, -1, -1, -1, -1], - [2, 3, 1, 0, 6, 2, 1, 3, -1, -1, -1, -1, -1, -1], + ("env", [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1]), + ("env", [1, 3, 0, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1]), + ("env", [1, 3, 1, 0, 6, 0, 0, 0, -1, -1, -1, -1, -1, -1]), + ("env", [2, 3, 1, 0, 6, 1, 2, 2, -1, -1, -1, -1, -1, -1]), + ("env", [2, 3, 1, 0, 6, 2, 1, 3, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [1, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [0, 0, 0, 0, 0, 1, 2, 2, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [0, 0, 0, 0, 0, 2, 1, 3, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [2, 3, 1, 0, 6, 1, 2, 2, -1, -1, -1, -1, -1, -1]), + ("env_sg_first", [2, 3, 1, 0, 6, 2, 1, 3, -1, -1, -1, -1, -1, -1]), ], ) def test__get_mask_invald_actions_backward__returns_expected_stage_transition( - env, state + env_input, state, request ): + env = request.getfixturevalue(env_input) stage = env._get_stage(state) mask = env.get_mask_invalid_actions_backward(state, done=False) for stg, subenv in env.subenvs.items(): - if stg == Stage.prev(stage) and stage != Stage(0): + if stg == env._get_previous_stage(stage) and stage != Stage(0): # Mask of done (EOS only) if stage is previous stage in state mask_subenv_expected = subenv.get_mask_invalid_actions_backward( env._get_state_of_subenv(state, stg), done=True @@ -470,21 +566,24 @@ def test__step__single_action_works(env, action): @pytest.mark.parametrize( - "actions, exp_result, exp_stage, last_action_valid", + "env_input, actions, exp_result, exp_stage, last_action_valid", [ [ + "env", [(1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2)], [0, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.COMPOSITION, True, ], [ + "env", [(2, 105, 3, -3, -3, -3, -3)], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.COMPOSITION, False, ], [ + "env", [ (1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), @@ -495,6 +594,7 @@ def test__step__single_action_works(env, action): True, ], [ + "env", [ (1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), @@ -506,6 +606,7 @@ def test__step__single_action_works(env, action): True, ], [ + "env", [ (1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), @@ -518,6 +619,7 @@ def test__step__single_action_works(env, action): False, ], [ + "env", [ (1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), @@ -530,6 +632,7 @@ def test__step__single_action_works(env, action): True, ], [ + "env", [ (1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), @@ -543,6 +646,7 @@ def test__step__single_action_works(env, action): False, ], [ + "env", [ (1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), @@ -556,6 +660,7 @@ def test__step__single_action_works(env, action): True, ], [ + "env", [ (1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), @@ -570,6 +675,7 @@ def test__step__single_action_works(env, action): False, ], [ + "env", [ (1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), @@ -584,6 +690,7 @@ def test__step__single_action_works(env, action): True, ], [ + "env", [ (1, 1, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), @@ -598,11 +705,124 @@ def test__step__single_action_works(env, action): Stage.LATTICE_PARAMETERS, True, ], + [ + "env_sg_first", + [(1, 1, -2, -2, -2, -2, -2)], + [1, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.SPACE_GROUP, + False, + ], + [ + "env_sg_first", + [(2, 105, 0, -3, -3, -3, -3)], + [1, 0, 0, 0, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.SPACE_GROUP, + True, + ], + [ + "env_sg_first", + [(2, 105, 0, -3, -3, -3, -3), (2, 105, 0, -3, -3, -3, -3)], + [1, 0, 0, 0, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.SPACE_GROUP, + False, + ], + [ + "env_sg_first", + [(2, 105, 0, -3, -3, -3, -3), (-1, -1, -1, -3, -3, -3, -3)], + [0, 0, 0, 0, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.COMPOSITION, + False, + ], + [ + "env_sg_first", + [ + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (3, 4, -2, -2, -2, -2, -2), + ], + [0, 0, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.COMPOSITION, + True, + ], + [ + "env_sg_first", + [ + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + ], + [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.LATTICE_PARAMETERS, + True, + ], + [ + "env_sg_first", + [ + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (1.5, 0, 0, 0, 0, 0, 0), + ], + [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.LATTICE_PARAMETERS, + False, + ], + [ + "env_sg_first", + [ + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), + (0.6, 0.5, 0.8, 0.3, 0.2, 0.6, 0), + ], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], + Stage.LATTICE_PARAMETERS, + False, + ], + [ + "env_sg_first", + [ + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (0.1, 0.1, 0.3, 0.0, 0.0, 0.0, 1), + (0.66, 0.0, 0.44, 0.0, 0.0, 0.0, 0), + ], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], + Stage.LATTICE_PARAMETERS, + True, + ], + [ + "env_sg_first", + [ + (2, 105, 0, -3, -3, -3, -3), + (-1, -1, -1, -3, -3, -3, -3), + (1, 1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (-1, -1, -2, -2, -2, -2, -2), + (0.1, 0.1, 0.3, 0.0, 0.0, 0.0, 1), + (0.66, 0.66, 0.44, 0.0, 0.0, 0.0, 0), + (np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf), + ], + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], + Stage.LATTICE_PARAMETERS, + True, + ], ], ) def test__step__action_sequence_has_expected_result( - env, actions, exp_result, exp_stage, last_action_valid + env_input, actions, exp_result, exp_stage, last_action_valid, request ): + env = request.getfixturevalue(env_input) for action in actions: warnings.filterwarnings("ignore") _, _, valid = env.step(action) @@ -613,9 +833,10 @@ def test__step__action_sequence_has_expected_result( @pytest.mark.parametrize( - "state_init, state_end, stage_init, stage_end, actions, last_action_valid", + "env_input, state_init, state_end, stage_init, stage_end, actions, last_action_valid", [ [ + "env", [0, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.COMPOSITION, @@ -624,6 +845,7 @@ def test__step__action_sequence_has_expected_result( True, ], [ + "env", [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.COMPOSITION, @@ -632,6 +854,7 @@ def test__step__action_sequence_has_expected_result( False, ], [ + "env", [1, 1, 0, 4, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.SPACE_GROUP, @@ -644,6 +867,7 @@ def test__step__action_sequence_has_expected_result( True, ], [ + "env", [1, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.SPACE_GROUP, @@ -657,6 +881,7 @@ def test__step__action_sequence_has_expected_result( True, ], [ + "env", [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.LATTICE_PARAMETERS, @@ -671,6 +896,7 @@ def test__step__action_sequence_has_expected_result( True, ], [ + "env", [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], Stage.LATTICE_PARAMETERS, @@ -681,6 +907,7 @@ def test__step__action_sequence_has_expected_result( False, ], [ + "env", [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.LATTICE_PARAMETERS, @@ -696,6 +923,7 @@ def test__step__action_sequence_has_expected_result( True, ], [ + "env", [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.LATTICE_PARAMETERS, @@ -712,6 +940,7 @@ def test__step__action_sequence_has_expected_result( True, ], [ + "env", [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], [0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], Stage.LATTICE_PARAMETERS, @@ -728,11 +957,88 @@ def test__step__action_sequence_has_expected_result( ], True, ], + [ + "env_sg_first", + [1, 0, 0, 0, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.SPACE_GROUP, + Stage.SPACE_GROUP, + [ + (2, 105, 0, -3, -3, -3, -3), + ], + True, + ], + [ + "env_sg_first", + [0, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 0, 0, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + Stage.COMPOSITION, + Stage.SPACE_GROUP, + [ + (3, 4, -2, -2, -2, -2, -2), + (1, 1, -2, -2, -2, -2, -2), + (-1, -1, -1, -3, -3, -3, -3), + ], + True, + ], + [ + "env_sg_first", + [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [1, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.LATTICE_PARAMETERS, + Stage.SPACE_GROUP, + [ + (-1, -1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (1, 1, -2, -2, -2, -2, -2), + (-1, -1, -1, -3, -3, -3, -3), + (2, 105, 0, -3, -3, -3, -3), + ], + True, + ], + [ + "env_sg_first", + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], + [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], + Stage.LATTICE_PARAMETERS, + Stage.LATTICE_PARAMETERS, + [ + (1.5, 0, 0, 0, 0, 0, 0), + ], + False, + ], + [ + "env_sg_first", + [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], + [1, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1], + Stage.LATTICE_PARAMETERS, + Stage.SPACE_GROUP, + [ + (np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf), + (0.66, 0.0, 0.44, 0.0, 0.0, 0.0, 0), + (0.1, 0.1, 0.3, 0.0, 0.0, 0.0, 1), + (-1, -1, -2, -2, -2, -2, -2), + (3, 4, -2, -2, -2, -2, -2), + (1, 1, -2, -2, -2, -2, -2), + (-1, -1, -1, -3, -3, -3, -3), + (2, 105, 0, -3, -3, -3, -3), + ], + True, + ], ], ) def test__step_backwards__action_sequence_has_expected_result( - env, state_init, state_end, stage_init, stage_end, actions, last_action_valid + env_input, + state_init, + state_end, + stage_init, + stage_end, + actions, + last_action_valid, + request, ): + env = request.getfixturevalue(env_input) + # Hacky way to also test if first action global EOS if actions[0] == env.eos: env.set_state(state_init, done=True) @@ -1294,3 +1600,8 @@ def test__continuous_env_with_stoichiometry_sg_check_common( ): print("\n\nCommon tests for crystal with composition <-> space group constraints\n") return common.test__continuous_env_common(env_with_stoichiometry_sg_check) + + +def test__continuous_env_common(env_sg_first): + print("\n\nCommon tests for crystal with space group first\n") + return common.test__continuous_env_common(env_sg_first) From 680b1cb7a545b0449a1f146970d525028d8c5a0c Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 23 Oct 2023 13:11:05 -0400 Subject: [PATCH 186/205] Fix sg and composition checks flags --- gflownet/envs/crystals/ccrystal.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index bbf3d5251..f35fde1cd 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -64,17 +64,21 @@ def __init__( do_sg_before_composition: bool = False, **kwargs, ): - do_composition_sg_checks = ( + self.do_sg_to_composition_constraints = ( do_sg_to_composition_constraints and do_sg_before_composition ) + self.do_composition_to_sg_constraints = ( + do_composition_to_sg_constraints and not do_sg_before_composition + ) + self.do_sg_to_lp_constraints = do_sg_to_lp_constraints + self.do_sg_before_composition = do_sg_before_composition + self.composition_kwargs = dict( - composition_kwargs or {}, do_spacegroup_check=do_composition_sg_checks + composition_kwargs or {}, + do_spacegroup_check=self.do_sg_to_composition_constraints, ) self.space_group_kwargs = space_group_kwargs or {} self.lattice_parameters_kwargs = lattice_parameters_kwargs or {} - self.do_composition_to_sg_constraints = do_composition_to_sg_constraints - self.do_sg_to_lp_constraints = do_sg_to_lp_constraints - self.do_sg_before_composition = do_sg_before_composition composition = Composition(**self.composition_kwargs) space_group = SpaceGroup(**self.space_group_kwargs) From b4972c7385915aa75844cf10fa955f080681505e Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Tue, 31 Oct 2023 06:58:23 -0400 Subject: [PATCH 187/205] Fix issue in set_state() --- gflownet/envs/crystals/ccrystal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index f35fde1cd..d88ee6d92 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -954,7 +954,7 @@ def set_state(self, state: List, done: Optional[bool] = False): elif stage is Stage.DONE: for subenv in done_subenvs: done_subenvs[subenv] = True - done_subenvs[-1] = done + done_subenvs[Stage.LATTICE_PARAMETERS] = done # Set state and done of each sub-environment for (stage, subenv), subenv_done in zip(self.subenvs.items(), done_subenvs): From 0cc8b58624bae7f1d19cc4c007f44da54b1a9c40 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Thu, 2 Nov 2023 07:40:23 -0400 Subject: [PATCH 188/205] Ensure that the environment starts in the correct stage --- gflownet/envs/crystals/ccrystal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index d88ee6d92..aa51d0b4e 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -286,7 +286,7 @@ def reset(self, env_id: Union[int, str] = None): ) super().reset(env_id=env_id) - self._set_stage(Stage.COMPOSITION) + self._set_stage(self._get_next_stage(None)) return self From e05d51f08693a9ed428a4769ccfb94939a03c281 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Thu, 2 Nov 2023 07:41:45 -0400 Subject: [PATCH 189/205] Fix typo --- gflownet/envs/crystals/ccrystal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index aa51d0b4e..a81c1f3e4 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -610,7 +610,7 @@ def step( ) elif stage is Stage.COMPOSITION: - if self.do_sg_before_composition and self.do_stoichiometry_sg_check: + if self.do_sg_before_composition and self.do_sg_to_composition_constraints: space_group = self.subenvs[Stage.SPACE_GROUP].space_group self.subenvs[Stage.COMPOSITION].space_group = space_group From bbaf1b345bff367c2fd5a94494aae5f6322a76c3 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Thu, 2 Nov 2023 07:46:07 -0400 Subject: [PATCH 190/205] Fix tests updates for spacegroup first --- tests/gflownet/envs/test_ccrystal.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index b14242f3c..962454c92 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -81,10 +81,10 @@ def test__stage_prev__returns_expected(env, env_sg_first): assert env._get_previous_stage(Stage.LATTICE_PARAMETERS) == Stage.SPACE_GROUP assert env._get_previous_stage(Stage.DONE) == Stage.LATTICE_PARAMETERS - assert env_sg_first._get_previous_stage(Stage.COMPOSITION) == Stage.DONE - assert env_sg_first._get_previous_stage(Stage.SPACE_GROUP) == Stage.COMPOSITION + assert env_sg_first._get_previous_stage(Stage.SPACE_GROUP) == Stage.DONE + assert env_sg_first._get_previous_stage(Stage.COMPOSITION) == Stage.SPACE_GROUP assert ( - env_sg_first._get_previous_stage(Stage.LATTICE_PARAMETERS) == Stage.SPACE_GROUP + env_sg_first._get_previous_stage(Stage.LATTICE_PARAMETERS) == Stage.COMPOSITION ) assert env_sg_first._get_previous_stage(Stage.DONE) == Stage.LATTICE_PARAMETERS @@ -100,9 +100,9 @@ def test__environment__has_expected_initial_state(env_input, initial_stage, requ the continuous lattice parameters environment is all -1s. """ env = request.getfixturevalue(env_input) - expected_initial_state = [initial_stage] + [0] * (4 + 3 + 6) + expected_initial_state = [initial_stage] + [0] * (4 + 3) + [-1] * 6 assert ( - env.state == env.source == [0] * (1 + 4 + 3) + [-1] * 6 + env.state == env.source == expected_initial_state ) # stage + n elements + space groups + lattice parameters @@ -731,7 +731,7 @@ def test__step__single_action_works(env, action): [(2, 105, 0, -3, -3, -3, -3), (-1, -1, -1, -3, -3, -3, -3)], [0, 0, 0, 0, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], Stage.COMPOSITION, - False, + True, ], [ "env_sg_first", From 3cc692dff53d8d118212fddef91d36029c2aeaa3 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Thu, 2 Nov 2023 08:20:12 -0400 Subject: [PATCH 191/205] Account for composition constraints in tests for spacegroup first --- tests/gflownet/envs/test_ccrystal.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 962454c92..d2645d322 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -749,11 +749,11 @@ def test__step__single_action_works(env, action): [ (2, 105, 0, -3, -3, -3, -3), (-1, -1, -1, -3, -3, -3, -3), - (1, 1, -2, -2, -2, -2, -2), + (1, 2, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), (-1, -1, -2, -2, -2, -2, -2), ], - [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [2, 2, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], Stage.LATTICE_PARAMETERS, True, ], @@ -762,12 +762,12 @@ def test__step__single_action_works(env, action): [ (2, 105, 0, -3, -3, -3, -3), (-1, -1, -1, -3, -3, -3, -3), - (1, 1, -2, -2, -2, -2, -2), + (1, 2, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), (-1, -1, -2, -2, -2, -2, -2), (1.5, 0, 0, 0, 0, 0, 0), ], - [2, 1, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], + [2, 2, 0, 4, 0, 4, 3, 105, -1, -1, -1, -1, -1, -1], Stage.LATTICE_PARAMETERS, False, ], @@ -776,13 +776,13 @@ def test__step__single_action_works(env, action): [ (2, 105, 0, -3, -3, -3, -3), (-1, -1, -1, -3, -3, -3, -3), - (1, 1, -2, -2, -2, -2, -2), + (1, 2, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), (-1, -1, -2, -2, -2, -2, -2), (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1), (0.6, 0.5, 0.8, 0.3, 0.2, 0.6, 0), ], - [2, 1, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], + [2, 2, 0, 4, 0, 4, 3, 105, 0.1, 0.1, 0.3, 0.4, 0.4, 0.4], Stage.LATTICE_PARAMETERS, False, ], @@ -791,13 +791,13 @@ def test__step__single_action_works(env, action): [ (2, 105, 0, -3, -3, -3, -3), (-1, -1, -1, -3, -3, -3, -3), - (1, 1, -2, -2, -2, -2, -2), + (1, 2, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), (-1, -1, -2, -2, -2, -2, -2), (0.1, 0.1, 0.3, 0.0, 0.0, 0.0, 1), (0.66, 0.0, 0.44, 0.0, 0.0, 0.0, 0), ], - [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], + [2, 2, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], Stage.LATTICE_PARAMETERS, True, ], @@ -806,14 +806,14 @@ def test__step__single_action_works(env, action): [ (2, 105, 0, -3, -3, -3, -3), (-1, -1, -1, -3, -3, -3, -3), - (1, 1, -2, -2, -2, -2, -2), + (1, 2, -2, -2, -2, -2, -2), (3, 4, -2, -2, -2, -2, -2), (-1, -1, -2, -2, -2, -2, -2), (0.1, 0.1, 0.3, 0.0, 0.0, 0.0, 1), (0.66, 0.66, 0.44, 0.0, 0.0, 0.0, 0), (np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf), ], - [2, 1, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], + [2, 2, 0, 4, 0, 4, 3, 105, 0.76, 0.76, 0.74, 0.4, 0.4, 0.4], Stage.LATTICE_PARAMETERS, True, ], From 69ab2dcb97dceb9292e4a14640053c40116c5ad4 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Thu, 2 Nov 2023 13:39:25 -0400 Subject: [PATCH 192/205] Fix ccrystal forward mask invalid actions --- gflownet/envs/crystals/ccrystal.py | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index a81c1f3e4..51066ab92 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -461,21 +461,26 @@ def get_mask_invalid_actions_forward( ) -> List[bool]: """ Computes the forward actions mask of the state. - - The mask of the parent crystal is simply the concatenation of the masks of the - three sub-environments. This assumes that the methods that will use the mask - will extract the part corresponding to the relevant stage and ignore the rest. """ state = self._get_state(state) + stage = self._get_stage(state) done = self._get_done(done) mask = [] - for stage, subenv in self.subenvs.items(): - mask.extend( - subenv.get_mask_invalid_actions_forward( - self._get_state_of_subenv(state, stage), done - ) + for subenv_stage, subenv in self.subenvs.items(): + # Get the mask of the current stage + subenv_mask = subenv.get_mask_invalid_actions_forward( + self._get_state_of_subenv(state, subenv_stage), done ) + + # If the subenv is not the current stage, make all actions invalid. + # TODO : We could save on computation by not calling + # _get_state_of_subenv() to generate these all-invalid masks + if subenv_stage != stage: + subenv_mask = [True] * len(subenv_mask) + + mask.extend(subenv_mask) + return mask # TODO: this piece of code looks awful From f481f891137814c85cfc9489f3a838e67a1914f8 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Thu, 2 Nov 2023 16:30:15 -0400 Subject: [PATCH 193/205] Fix backward masks in ccrystal --- gflownet/envs/crystals/ccrystal.py | 19 +++++++++++++++---- tests/gflownet/envs/test_ccrystal.py | 3 ++- 2 files changed, 17 insertions(+), 5 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 51066ab92..22108da3d 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -510,8 +510,13 @@ def get_mask_invalid_actions_backward( mask = [] do_eos_only = False + # Iterate stages in reverse order - for stg, subenv in reversed(self.subenvs.items()): + subenv_masks = {} + stg = self._get_previous_stage(Stage.DONE) + while stg != Stage.DONE: + subenv = self.subenvs[stg] + state_subenv = self._get_state_of_subenv(state, stg) # Set mask of done state because state of next subenv is source if do_eos_only: @@ -524,7 +529,8 @@ def get_mask_invalid_actions_backward( # stg is the current stage if stg == stage: # state of subenv is the source state - if stg != Stage(0) and state_subenv == subenv.source: + prev_stg = self._get_previous_stage(stg) + if prev_stg != Stage.DONE and state_subenv == subenv.source: do_eos_only = True mask_subenv = subenv.get_mask_invalid_actions_backward( subenv.source @@ -539,8 +545,13 @@ def get_mask_invalid_actions_backward( mask_subenv = subenv.get_mask_invalid_actions_backward( subenv.source ) - mask.extend(mask_subenv[::-1]) - return mask[::-1] + subenv_masks[stg] = mask_subenv + stg = self._get_previous_stage(stg) + + # Combine the individual masks to produce the global mask + for stg, subenv in self.subenvs.items(): + mask.extend(subenv_masks[stg]) + return mask def _update_state(self, stage: Stage): """ diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index d2645d322..1811e70f7 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -539,9 +539,10 @@ def test__get_mask_invald_actions_backward__returns_expected_stage_transition( ): env = request.getfixturevalue(env_input) stage = env._get_stage(state) + prev_stage = env._get_previous_stage(stage) mask = env.get_mask_invalid_actions_backward(state, done=False) for stg, subenv in env.subenvs.items(): - if stg == env._get_previous_stage(stage) and stage != Stage(0): + if stg == prev_stage and prev_stage != Stage.DONE: # Mask of done (EOS only) if stage is previous stage in state mask_subenv_expected = subenv.get_mask_invalid_actions_backward( env._get_state_of_subenv(state, stg), done=True From 873dac86385eeee9385e81273768f2a68cabed74 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Thu, 2 Nov 2023 16:36:11 -0400 Subject: [PATCH 194/205] Black --- gflownet/envs/crystals/ccrystal.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 22108da3d..23b677b89 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -626,7 +626,10 @@ def step( ) elif stage is Stage.COMPOSITION: - if self.do_sg_before_composition and self.do_sg_to_composition_constraints: + if ( + self.do_sg_before_composition + and self.do_sg_to_composition_constraints + ): space_group = self.subenvs[Stage.SPACE_GROUP].space_group self.subenvs[Stage.COMPOSITION].space_group = space_group From 49a9510baa6719b0a2c6306001b3487dc4dcbc52 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 6 Nov 2023 08:59:27 -0500 Subject: [PATCH 195/205] Fix failing common tests --- gflownet/envs/crystals/ccrystal.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gflownet/envs/crystals/ccrystal.py b/gflownet/envs/crystals/ccrystal.py index 23b677b89..0336d8a43 100644 --- a/gflownet/envs/crystals/ccrystal.py +++ b/gflownet/envs/crystals/ccrystal.py @@ -753,7 +753,7 @@ def sample_actions_batch( # stage so that EOS of preceding stage is sampled. if ( is_backward - and stage != Stage(0) + and self._get_previous_stage(stage) != Stage.DONE and state_subenv == self.subenvs[stage].source ): stage = self._get_previous_stage(stage) @@ -832,7 +832,7 @@ def get_logprobs( # stage so that EOS of preceding stage is sampled. if ( is_backward - and stage != Stage(0) + and self._get_previous_stage(stage) != Stage.DONE and state_subenv == self.subenvs[stage].source ): stage = self._get_previous_stage(stage) From 061105a4dbb4dd10d92842289e3247590c60572a Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 6 Nov 2023 10:01:21 -0500 Subject: [PATCH 196/205] Fix ccrystal set_state test --- tests/gflownet/envs/test_ccrystal.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 1811e70f7..4ec2ae83a 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -435,14 +435,13 @@ def test__set_state__sets_state_subenvs_dones_and_constraints( assert subenv.done == done # Check lattice parameters - if env.subenvs[Stage.SPACE_GROUP].lattice_system != "None": - assert has_lattice_parameters + if has_lattice_parameters: + assert env.subenvs[Stage.SPACE_GROUP].lattice_system != "None" assert ( env.subenvs[Stage.SPACE_GROUP].lattice_system == env.subenvs[Stage.LATTICE_PARAMETERS].lattice_system ) - else: - assert not has_lattice_parameters + # Check composition constraints if has_composition_constraints: From b1c2c8820f06804399ab43e244dfc232c8c19a61 Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Mon, 6 Nov 2023 10:02:13 -0500 Subject: [PATCH 197/205] Black --- tests/gflownet/envs/test_ccrystal.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/gflownet/envs/test_ccrystal.py b/tests/gflownet/envs/test_ccrystal.py index 4ec2ae83a..e88a348ae 100644 --- a/tests/gflownet/envs/test_ccrystal.py +++ b/tests/gflownet/envs/test_ccrystal.py @@ -442,7 +442,6 @@ def test__set_state__sets_state_subenvs_dones_and_constraints( == env.subenvs[Stage.LATTICE_PARAMETERS].lattice_system ) - # Check composition constraints if has_composition_constraints: n_atoms = [n for n in env.subenvs[Stage.COMPOSITION].state if n > 0] From 4d21a666039bd0dc4d2ddadb5e292092cf34409b Mon Sep 17 00:00:00 2001 From: Pierre Luc Carrier Date: Thu, 16 Nov 2023 15:34:13 -0500 Subject: [PATCH 198/205] Add config file for experiment with spacegroup first --- .../crystals/albatross_sg_first.yaml | 110 ++++++++++++++++++ 1 file changed, 110 insertions(+) create mode 100644 config/experiments/crystals/albatross_sg_first.yaml diff --git a/config/experiments/crystals/albatross_sg_first.yaml b/config/experiments/crystals/albatross_sg_first.yaml new file mode 100644 index 000000000..431f7a4e8 --- /dev/null +++ b/config/experiments/crystals/albatross_sg_first.yaml @@ -0,0 +1,110 @@ +# @package _global_ + +defaults: + - override /env: crystals/ccrystal + - override /gflownet: trajectorybalance + - override /proxy: crystals/dave + - override /logger: wandb + +device: cpu + +# Environment +env: + do_composition_to_sg_constraints: False, + do_sg_to_composition_constraints: True, + # do_sg_to_lp_constraints: True, + do_sg_before_composition: True, + composition_kwargs: + elements: [1, 3, 6, 7, 8, 9, 12, 14, 15, 16, 17, 26] + min_atoms: 2 + max_atoms: 50 + min_atom_i: 1 + max_atom_i: 16 + do_charge_check: True + space_group_kwargs: + space_groups_subset: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 25, 26, 29, 30, 31, 33, 36, 38, 40, 41, 43, 44, 46, 47, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 82, 84, 85, 86, 87, 88, 92, 99, 102, 107, 113, 114, 121, 122, 123, 126, 129, 131, 136, 137, 138, 139, 140, 141, 146, 147, 148, 150, 155, 156, 160, 161, 162, 163, 164, 166, 167, 176, 181, 185, 186, 187, 189, 192, 194, 198, 199, 205, 206, 216, 217, 220, 221, 224, 225, 227, 229, 230] + lattice_parameters_kwargs: + min_length: 0.9 + max_length: 100.0 + min_angle: 50.0 + max_angle: 150.0 + n_comp: 5 + beta_params_min: 0.1 + beta_params_max: 100.0 + min_incr: 0.1 + fixed_distr_params: + beta_weights: 1.0 + beta_alpha: 10.0 + beta_beta: 10.0 + bernoulli_eos_prob: 0.1 + bernoulli_bts_prob: 0.1 + random_distr_params: + beta_weights: 1.0 + beta_alpha: 10.0 + beta_beta: 10.0 + bernoulli_eos_prob: 0.1 + bernoulli_bts_prob: 0.1 + reward_func: boltzmann + reward_beta: 8 + buffer: + replay_capacity: 0 + test: + type: pkl + path: /home/mila/h/hernanga/gflownet/data/crystals/matbench_normed_l0.9-100_a50-150_val_12_SGinter_states_energy.pkl + output_csv: ccrystal_val.csv + output_pkl: ccrystal_val.pkl + +# GFlowNet hyperparameters +gflownet: + random_action_prob: 0.1 + optimizer: + batch_size: + forward: 10 + backward_replay: -1 + lr: 0.0001 + z_dim: 16 + lr_z_mult: 100 + n_train_steps: 50000 + lr_decay_period: 1000000 + replay_sampling: weighted + +# Policy +policy: + forward: + type: mlp + n_hid: 256 + n_layers: 3 + checkpoint: forward + backward: + type: mlp + n_hid: 256 + n_layers: 3 + shared_weights: False + checkpoint: backward + +# WandB +logger: + lightweight: True + project_name: "crystal-gfn" + tags: + - gflownet + - crystals + - matbench + - workshop23 + checkpoints: + period: 500 + do: + online: true + test: + n_trajs_logprobs: 10 + period: 500 + n: 10 + n_top_k: 5000 + top_k: 100 + top_k_period: -1 + +# Hydra +hydra: + run: + dir: ${user.logdir.root}/workshop23/discrete-matbench/${oc.env:SLURM_JOB_ID,local}/${now:%Y-%m-%d_%H-%M-%S} + From 7413040bc6ef6e82e28052c9930ec72f77562bdd Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 16 Nov 2023 16:29:46 -0500 Subject: [PATCH 199/205] Update ccube config files because policy config is not being loaded as expected --- config/experiments/ccube/corners.yaml | 23 +++++++++++------------ config/experiments/ccube/uniform.yaml | 23 +++++++++++------------ 2 files changed, 22 insertions(+), 24 deletions(-) diff --git a/config/experiments/ccube/corners.yaml b/config/experiments/ccube/corners.yaml index e3594ac76..ccc207c6f 100644 --- a/config/experiments/ccube/corners.yaml +++ b/config/experiments/ccube/corners.yaml @@ -40,18 +40,17 @@ gflownet: z_dim: 16 lr_z_mult: 100 n_train_steps: 10000 - policy: - forward: - type: mlp - n_hid: 512 - n_layers: 5 - checkpoint: forward - backward: - type: mlp - n_hid: 512 - n_layers: 5 - shared_weights: False - checkpoint: backward + +# Policy +policy: + forward: + type: mlp + n_hid: 128 + n_layers: 2 + checkpoint: forward + backward: + shared_weights: True + checkpoint: backward # WandB logger: diff --git a/config/experiments/ccube/uniform.yaml b/config/experiments/ccube/uniform.yaml index 6970a3e95..a81d58d05 100644 --- a/config/experiments/ccube/uniform.yaml +++ b/config/experiments/ccube/uniform.yaml @@ -40,18 +40,17 @@ gflownet: z_dim: 16 lr_z_mult: 100 n_train_steps: 10000 - policy: - forward: - type: mlp - n_hid: 256 - n_layers: 3 - checkpoint: forward - backward: - type: mlp - n_hid: 256 - n_layers: 3 - shared_weights: False - checkpoint: backward + +# Policy +policy: + forward: + type: mlp + n_hid: 128 + n_layers: 2 + checkpoint: forward + backward: + shared_weights: True + checkpoint: backward # WandB logger: From a169a9e8ebc0cde854b64a44d522b25abb5fc2e5 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 16 Nov 2023 16:31:55 -0500 Subject: [PATCH 200/205] Update clatticeparameters config files because policy config is not being loaded as expected --- .../clatticeparams/clatticeparams_owl.yaml | 26 ++++++++++--------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/config/experiments/clatticeparams/clatticeparams_owl.yaml b/config/experiments/clatticeparams/clatticeparams_owl.yaml index 2f9c6ee0e..30f1c1347 100644 --- a/config/experiments/clatticeparams/clatticeparams_owl.yaml +++ b/config/experiments/clatticeparams/clatticeparams_owl.yaml @@ -45,18 +45,20 @@ gflownet: z_dim: 16 lr_z_mult: 100 n_train_steps: 10000 - policy: - forward: - type: mlp - n_hid: 512 - n_layers: 5 - checkpoint: forward - backward: - type: mlp - n_hid: 512 - n_layers: 5 - shared_weights: False - checkpoint: backward + +# Policy +policy: + forward: + type: mlp + n_hid: 512 + n_layers: 5 + checkpoint: forward + backward: + type: mlp + n_hid: 512 + n_layers: 5 + shared_weights: False + checkpoint: backward # WandB logger: From 7ef6764af604cf90a776980338ff56cdc4752fec Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 16 Nov 2023 16:40:19 -0500 Subject: [PATCH 201/205] Remove goose and penguin configs --- config/experiments/crystals/goose.yaml | 85 ------------------- config/experiments/crystals/penguin.yaml | 103 ----------------------- 2 files changed, 188 deletions(-) delete mode 100644 config/experiments/crystals/goose.yaml delete mode 100644 config/experiments/crystals/penguin.yaml diff --git a/config/experiments/crystals/goose.yaml b/config/experiments/crystals/goose.yaml deleted file mode 100644 index 37e331521..000000000 --- a/config/experiments/crystals/goose.yaml +++ /dev/null @@ -1,85 +0,0 @@ -# @package _global_ - -defaults: - - override /env: crystals/ccrystal - - override /gflownet: trajectorybalance - - override /proxy: crystals/dave - - override /logger: wandb - -device: cpu - -# Environment -env: - composition_kwargs: - elements: [1, 3, 6, 7, 8, 9, 12, 14, 15, 16, 17, 26] - max_atoms: 50 - max_atom_i: 16 - space_group_kwargs: - space_groups_subset: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 25, 26, 29, 30, 31, 33, 36, 38, 40, 41, 43, 44, 46, 47, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 82, 84, 85, 86, 87, 88, 92, 99, 102, 107, 113, 114, 121, 122, 123, 126, 129, 131, 136, 137, 138, 139, 140, 141, 146, 147, 148, 150, 155, 156, 160, 161, 162, 163, 164, 166, 167, 176, 181, 185, 186, 187, 189, 192, 194, 198, 199, 205, 206, 216, 217, 220, 221, 224, 225, 227, 229, 230] - lattice_parameters_kwargs: - min_length: 0.9 - max_length: 100.0 - min_angle: 50.0 - max_angle: 150.0 - reward_func: boltzmann - reward_beta: 1 - buffer: - replay_capacity: 0 - test: - type: pkl - path: /home/mila/h/hernanga/gflownet/data/crystals/matbench_normed_l0.9-100_a50-150_val_12_SGinter_states_energy.pkl - output_csv: ccrystal_val.csv - output_pkl: ccrystal_val.pkl - -# GFlowNet hyperparameters -gflownet: - random_action_prob: 0.1 - optimizer: - batch_size: - forward: 10 - backward_replay: -1 - lr: 0.001 - z_dim: 16 - lr_z_mult: 100 - n_train_steps: 10000 - lr_decay_period: 1000000 - replay_sampling: weighted - policy: - forward: - type: mlp - n_hid: 512 - n_layers: 5 - checkpoint: forward - backward: - type: mlp - n_hid: 512 - n_layers: 5 - shared_weights: False - checkpoint: backward - -# WandB -logger: - lightweight: True - project_name: "crystal-gfn" - tags: - - gflownet - - crystals - - matbench - - workshop23 - checkpoints: - period: 500 - do: - online: true - test: - n_trajs_logprobs: 10 - period: 100 - n: 10 - n_top_k: 5000 - top_k: 100 - top_k_period: -1 - -# Hydra -hydra: - run: - dir: ${user.logdir.root}/workshop23/discrete-matbench/${oc.env:SLURM_JOB_ID,local}/${now:%Y-%m-%d_%H-%M-%S} - diff --git a/config/experiments/crystals/penguin.yaml b/config/experiments/crystals/penguin.yaml deleted file mode 100644 index fb1eedaec..000000000 --- a/config/experiments/crystals/penguin.yaml +++ /dev/null @@ -1,103 +0,0 @@ -# @package _global_ - -defaults: - - override /env: crystals/ccrystal - - override /gflownet: trajectorybalance - - override /proxy: crystals/dave - - override /logger: wandb - -device: cpu - -# Environment -env: - composition_kwargs: - elements: [1, 3, 6, 7, 8, 9, 12, 14, 15, 16, 17, 26] - max_atoms: 50 - max_atom_i: 16 - space_group_kwargs: - space_groups_subset: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 25, 26, 29, 30, 31, 33, 36, 38, 40, 41, 43, 44, 46, 47, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 82, 84, 85, 86, 87, 88, 92, 99, 102, 107, 113, 114, 121, 122, 123, 126, 129, 131, 136, 137, 138, 139, 140, 141, 146, 147, 148, 150, 155, 156, 160, 161, 162, 163, 164, 166, 167, 176, 181, 185, 186, 187, 189, 192, 194, 198, 199, 205, 206, 216, 217, 220, 221, 224, 225, 227, 229, 230] - lattice_parameters_kwargs: - min_length: 0.9 - max_length: 100.0 - min_angle: 50.0 - max_angle: 150.0 - n_comp: 5 - beta_params_min: 0.1 - beta_params_max: 100.0 - min_incr: 0.1 - fixed_distr_params: - beta_weights: 1.0 - beta_alpha: 10.0 - beta_beta: 10.0 - bernoulli_eos_prob: 0.1 - bernoulli_bts_prob: 0.1 - random_distr_params: - beta_weights: 1.0 - beta_alpha: 10.0 - beta_beta: 10.0 - bernoulli_eos_prob: 0.1 - bernoulli_bts_prob: 0.1 - reward_func: boltzmann - reward_beta: 1 - buffer: - replay_capacity: 0 - test: - type: pkl - path: /home/mila/h/hernanga/gflownet/data/crystals/matbench_normed_l0.9-100_a50-150_val_12_SGinter_states_energy.pkl - output_csv: ccrystal_val.csv - output_pkl: ccrystal_val.pkl - -# GFlowNet hyperparameters -gflownet: - random_action_prob: 0.1 - optimizer: - batch_size: - forward: 10 - backward_replay: -1 - lr: 0.0001 - z_dim: 16 - lr_z_mult: 100 - n_train_steps: 25000 - lr_decay_period: 1000000 - replay_sampling: weighted - -# Policy -policy: - forward: - type: mlp - n_hid: 256 - n_layers: 3 - checkpoint: forward - backward: - type: mlp - n_hid: 256 - n_layers: 3 - shared_weights: False - checkpoint: backward - -# WandB -logger: - lightweight: True - project_name: "crystal-gfn" - tags: - - gflownet - - crystals - - matbench - - workshop23 - checkpoints: - period: 500 - do: - online: true - test: - n_trajs_logprobs: 10 - period: 500 - n: 10 - n_top_k: 5000 - top_k: 100 - top_k_period: -1 - -# Hydra -hydra: - run: - dir: ${user.logdir.root}/workshop23/discrete-matbench/${oc.env:SLURM_JOB_ID,local}/${now:%Y-%m-%d_%H-%M-%S} - From 619281ddc8c4aa0f12254caef539e1a847c0aa1f Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 16 Nov 2023 16:41:43 -0500 Subject: [PATCH 202/205] Update discrete-matbench config files because policy config is not being loaded as expected --- .../workshop23/discrete-matbench.yaml | 26 ++++++++++--------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/config/experiments/workshop23/discrete-matbench.yaml b/config/experiments/workshop23/discrete-matbench.yaml index 7816d2e51..a68b0c367 100644 --- a/config/experiments/workshop23/discrete-matbench.yaml +++ b/config/experiments/workshop23/discrete-matbench.yaml @@ -36,18 +36,20 @@ gflownet: n_train_steps: 10000 lr_decay_period: 1000000 replay_sampling: weighted - policy: - forward: - type: mlp - n_hid: 512 - n_layers: 5 - checkpoint: forward - backward: - type: mlp - n_hid: 512 - n_layers: 5 - shared_weights: False - checkpoint: backward + +# Policy +policy: + forward: + type: mlp + n_hid: 512 + n_layers: 5 + checkpoint: forward + backward: + type: mlp + n_hid: 512 + n_layers: 5 + shared_weights: False + checkpoint: backward # WandB logger: From 201150c09b12294ef74b49cc4ee63dc1da9064a1 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 16 Nov 2023 16:56:45 -0500 Subject: [PATCH 203/205] Fix: Change default time in launch.py to 0 (no time limit) --- mila/launch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mila/launch.py b/mila/launch.py index 5e2f5379f..9b122ec7f 100644 --- a/mila/launch.py +++ b/mila/launch.py @@ -406,7 +406,7 @@ def code_dir_for_slurm_tmp_dir_checkout(git_checkout): "outdir": "$SCRATCH/gflownet/logs/slurm", "partition": "long", "template": "$root/mila/sbatch/template-conda.sh", - "time": None, + "time": "0", "venv": None, "verbose": False, } From 7ea8f9dfbf0bca2997bb3262ec0c3bd86f6a209e Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Thu, 16 Nov 2023 18:02:01 -0500 Subject: [PATCH 204/205] Fix: clone of policy_outputs into logits in get_logprobs must not be detached --- gflownet/envs/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gflownet/envs/base.py b/gflownet/envs/base.py index a14927c9d..62c06ecd0 100644 --- a/gflownet/envs/base.py +++ b/gflownet/envs/base.py @@ -546,7 +546,7 @@ def get_logprobs( """ device = policy_outputs.device ns_range = torch.arange(policy_outputs.shape[0]).to(device) - logits = policy_outputs.clone().detach() + logits = policy_outputs.clone() if mask is not None: logits[mask] = -torch.inf action_indices = ( From 15e77fa16952d057eb1edb32d5340d07dec05f24 Mon Sep 17 00:00:00 2001 From: alexhernandezgarcia Date: Mon, 27 Nov 2023 10:59:07 -0500 Subject: [PATCH 205/205] Small fixes in the evaluation --- gflownet/utils/buffer.py | 3 --- gflownet/utils/common.py | 15 +++++++++------ 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/gflownet/utils/buffer.py b/gflownet/utils/buffer.py index eb105613a..b5d3d3e42 100644 --- a/gflownet/utils/buffer.py +++ b/gflownet/utils/buffer.py @@ -217,9 +217,6 @@ def make_data_set(self, config): f", but only {n_samples_new} are valid according to the " "environment settings. Invalid samples have been discarded." ) - n_max = 100 - samples = samples[:n_max] - print(f"Only the first {n_max} samples will be kept in the data.") print("Remember to write a function to normalise the data in code") print("Max number of elements in data set has to match config") print("Actually, write a function that contrasts the stats") diff --git a/gflownet/utils/common.py b/gflownet/utils/common.py index 2e4a6b2d2..e7e7f8afd 100644 --- a/gflownet/utils/common.py +++ b/gflownet/utils/common.py @@ -94,13 +94,16 @@ def resolve_path(path: str) -> Path: return Path(expandvars(str(path))).expanduser().resolve() -def find_latest_checkpoint(ckpt_dir, pattern): - final = list(ckpt_dir.glob(f"{pattern}*final*")) +def find_latest_checkpoint(ckpt_dir, ckpt_name): + ckpt_name = Path(ckpt_name).stem + final = list(ckpt_dir.glob(f"{ckpt_name}*final*")) if len(final) > 0: return final[0] - ckpts = list(ckpt_dir.glob(f"{pattern}*")) + ckpts = list(ckpt_dir.glob(f"{ckpt_name}*")) if not ckpts: - raise ValueError(f"No checkpoints found in {ckpt_dir} with pattern {pattern}") + raise ValueError( + f"No final checkpoints found in {ckpt_dir} with pattern {ckpt_name}*final*" + ) return sorted(ckpts, key=lambda f: float(f.stem.split("iter")[1]))[-1] @@ -175,12 +178,12 @@ def load_gflow_net_from_run_path( # ------------------------------- ckpt = [f for f in run_path.rglob(config.logger.logdir.ckpts) if f.is_dir()][0] - forward_final = find_latest_checkpoint(ckpt, "pf") + forward_final = find_latest_checkpoint(ckpt, config.policy.forward.checkpoint) gflownet.forward_policy.model.load_state_dict( torch.load(forward_final, map_location=set_device(device)) ) try: - backward_final = find_latest_checkpoint(ckpt, "pb") + backward_final = find_latest_checkpoint(ckpt, config.policy.backward.checkpoint) gflownet.backward_policy.model.load_state_dict( torch.load(backward_final, map_location=set_device(device)) )