diff --git a/LICENSE b/LICENSE index f288702d..261eeb9e 100644 --- a/LICENSE +++ b/LICENSE @@ -1,674 +1,201 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. By contrast, -the GNU General Public License is intended to guarantee your freedom to -share and change all versions of a program--to make sure it remains free -software for all its users. We, the Free Software Foundation, use the -GNU General Public License for most of our software; it applies also to -any other work released this way by its authors. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/src/gfn/containers/trajectories.py b/src/gfn/containers/trajectories.py index 2b42eb2c..251b5921 100644 --- a/src/gfn/containers/trajectories.py +++ b/src/gfn/containers/trajectories.py @@ -72,7 +72,7 @@ def __init__( self.env = env self.is_backward = is_backward self.states = ( - states.clone() # TODO: Do we need this clone? + states if states is not None else env.states_from_batch_shape((0, 0)) ) @@ -160,9 +160,18 @@ def __getitem__(self, index: int | Sequence[int]) -> Trajectories: log_rewards = ( self._log_rewards[index] if self._log_rewards is not None else None ) - - if isinstance(self.estimator_outputs, Tensor): + if is_tensor(self.estimator_outputs): + # TODO: Is there a safer way to index self.estimator_outputs for + # for n-dimensional estimator outputs? + # + # First we index along the first dimension of the estimator outputs. + # This can be thought of as the instance dimension, and is + # compatible with all supported indexing approaches (dim=1). + # All dims > 1 are not explicitly indexed unless the dimensionality + # of `index` matches all dimensions of `estimator_outputs` aside + # from the first (trajectory) dimension. estimator_outputs = self.estimator_outputs[:, index] + # Next we index along the trajectory length (dim=0) estimator_outputs = estimator_outputs[:new_max_length] else: estimator_outputs = None @@ -211,6 +220,9 @@ def extend(self, other: Trajectories) -> None: Args: other: an external set of Trajectories. """ + if len(other) == 0: + return + # TODO: The replay buffer is storing `dones` - this wastes a lot of space. self.actions.extend(other.actions) self.states.extend(other.states) @@ -258,7 +270,7 @@ def extend(self, other: Trajectories) -> None: other_shape = np.array(other.estimator_outputs.shape) required_first_dim = max(self_shape[0], other_shape[0]) - # TODO: This should be a single reused function. + # TODO: This should be a single reused function (#154) # The size of self needs to grow to match other along dim=0. if self_shape[0] < other_shape[0]: pad_dim = required_first_dim - self_shape[0] diff --git a/src/gfn/env.py b/src/gfn/env.py index bd5861ed..588b2553 100644 --- a/src/gfn/env.py +++ b/src/gfn/env.py @@ -8,6 +8,7 @@ from gfn.actions import Actions from gfn.preprocessors import IdentityPreprocessor, Preprocessor from gfn.states import DiscreteStates, States +from gfn.utils.common import set_seed # Errors NonValidActionsError = type("NonValidActionsError", (ValueError,), {}) @@ -171,7 +172,7 @@ def reset( assert not (random and sink) if random and seed is not None: - torch.manual_seed(seed) # TODO: Improve seeding here? + set_seed(seed, performance_mode=True) if batch_shape is None: batch_shape = (1,) @@ -217,10 +218,9 @@ def _step( not_done_states = new_states[~new_sink_states_idx] not_done_actions = actions[~new_sink_states_idx] - new_not_done_states_tensor = self.step(not_done_states, not_done_actions) - # TODO: Why is this here? Should it be removed? - # if isinstance(new_states, DiscreteStates): - # new_not_done_states.masks = self.update_masks(not_done_states, not_done_actions) + new_not_done_states_tensor = self.maskless_step( + not_done_states, not_done_actions + ) new_states.tensor[~new_sink_states_idx] = new_not_done_states_tensor diff --git a/src/gfn/gflownet/base.py b/src/gfn/gflownet/base.py index e7d80921..5e04151d 100644 --- a/src/gfn/gflownet/base.py +++ b/src/gfn/gflownet/base.py @@ -26,12 +26,15 @@ class GFlowNet(ABC, nn.Module, Generic[TrainingSampleType]): """ @abstractmethod - def sample_trajectories(self, env: Env, n_samples: int) -> Trajectories: + def sample_trajectories( + self, env: Env, n_samples: int, sample_off_policy: bool + ) -> Trajectories: """Sample a specific number of complete trajectories. Args: env: the environment to sample trajectories from. n_samples: number of trajectories to be sampled. + sample_off_policy: whether to sample trajectories on / off policy. Returns: Trajectories: sampled trajectories object. """ @@ -48,12 +51,6 @@ def sample_terminating_states(self, env: Env, n_samples: int) -> States: trajectories = self.sample_trajectories(env, n_samples, sample_off_policy=False) return trajectories.last_states - def pf_pb_named_parameters(self): - return {k: v for k, v in self.named_parameters() if "pb" in k or "pf" in k} - - def pf_pb_parameters(self): - return [v for k, v in self.named_parameters() if "pb" in k or "pf" in k] - def logz_named_parameters(self): return {"logZ": dict(self.named_parameters())["logZ"]} @@ -97,6 +94,12 @@ def sample_trajectories( return trajectories + def pf_pb_named_parameters(self): + return {k: v for k, v in self.named_parameters() if "pb" in k or "pf" in k} + + def pf_pb_parameters(self): + return [v for k, v in self.named_parameters() if "pb" in k or "pf" in k] + class TrajectoryBasedGFlowNet(PFBasedGFlowNet[Trajectories]): def get_pfs_and_pbs( @@ -148,7 +151,7 @@ def get_pfs_and_pbs( if self.off_policy: # We re-use the values calculated in .sample_trajectories(). - if not isinstance(trajectories.estimator_outputs, type(None)): + if trajectories.estimator_outputs is not None: estimator_outputs = trajectories.estimator_outputs[ ~trajectories.actions.is_dummy ] @@ -211,9 +214,8 @@ def get_trajectories_scores( total_log_pb_trajectories = log_pb_trajectories.sum(dim=0) log_rewards = trajectories.log_rewards - if math.isfinite(self.log_reward_clip_min) and not isinstance( - log_rewards, type(None) - ): + # TODO: log_reward_clip_min isn't defined in base (#155). + if math.isfinite(self.log_reward_clip_min) and log_rewards is not None: log_rewards = log_rewards.clamp_min(self.log_reward_clip_min) if torch.any(torch.isinf(total_log_pf_trajectories)) or torch.any( diff --git a/src/gfn/gflownet/detailed_balance.py b/src/gfn/gflownet/detailed_balance.py index 818a2d8a..4cb4e6e2 100644 --- a/src/gfn/gflownet/detailed_balance.py +++ b/src/gfn/gflownet/detailed_balance.py @@ -35,12 +35,12 @@ def __init__( logF: ScalarEstimator, off_policy: bool, forward_looking: bool = False, - log_reward_clamp_min: float = -float("inf"), + log_reward_clip_min: float = -float("inf"), ): super().__init__(pf, pb, off_policy=off_policy) self.logF = logF self.forward_looking = forward_looking - self.log_reward_clamp_min = log_reward_clamp_min + self.log_reward_clip_min = log_reward_clip_min def get_scores( self, env: Env, transitions: Transitions @@ -68,10 +68,13 @@ def get_scores( if states.batch_shape != tuple(actions.batch_shape): raise ValueError("Something wrong happening with log_pf evaluations") - if self.off_policy: + if not self.off_policy: valid_log_pf_actions = transitions.log_probs else: # Evaluate the log PF of the actions sampled off policy. + # I suppose the Transitions container should then have some + # estimator_outputs attribute as well, to avoid duplication here ? + # See (#156). module_output = self.pf(states) # TODO: Inefficient duplication. valid_log_pf_actions = self.pf.to_probability_distribution( states, module_output @@ -82,8 +85,8 @@ def get_scores( valid_log_F_s = self.logF(states).squeeze(-1) if self.forward_looking: log_rewards = env.log_reward(states) # TODO: RM unsqueeze(-1) ? - if math.isfinite(self.log_reward_clamp_min): - log_rewards = log_rewards.clamp_min(self.log_reward_clamp_min) + if math.isfinite(self.log_reward_clip_min): + log_rewards = log_rewards.clamp_min(self.log_reward_clip_min) valid_log_F_s = valid_log_F_s + log_rewards preds = valid_log_pf_actions + valid_log_F_s @@ -163,7 +166,7 @@ def get_scores(self, transitions: Transitions) -> TT["n_trajectories", torch.flo all_log_rewards = transitions.all_log_rewards[mask] module_output = self.pf(states) pf_dist = self.pf.to_probability_distribution(states, module_output) - if self.off_policy: + if not self.off_policy: valid_log_pf_actions = transitions[mask].log_probs else: # Evaluate the log PF of the actions sampled off policy. diff --git a/src/gfn/samplers.py b/src/gfn/samplers.py index 10755915..68b052a6 100644 --- a/src/gfn/samplers.py +++ b/src/gfn/samplers.py @@ -91,7 +91,7 @@ def sample_trajectories( off_policy: bool, states: Optional[States] = None, n_trajectories: Optional[int] = None, - test_mode: bool = False, + debug_mode: bool = False, **policy_kwargs, ) -> Trajectories: """Sample trajectories sequentially. @@ -110,7 +110,7 @@ def sample_trajectories( parameter, `epsilon`, and `sf_bias`. In the continuous case these kwargs will be user defined. This can be used to, for example, sample off-policy. - test_mode: if True, everything gets calculated. + debug_mode: if True, everything gets calculated. Returns: A Trajectories object representing the batch of sampled trajectories. @@ -118,8 +118,8 @@ def sample_trajectories( AssertionError: When both states and n_trajectories are specified. AssertionError: When states are not linear. """ - save_estimator_outputs = off_policy or test_mode - skip_logprob_calculaion = off_policy and not test_mode + save_estimator_outputs = off_policy or debug_mode + skip_logprob_calculaion = off_policy and not debug_mode if states is None: assert ( @@ -171,7 +171,7 @@ def sample_trajectories( calculate_logprobs=False if skip_logprob_calculaion else True, **policy_kwargs, ) - if not isinstance(estimator_outputs, type(None)): + if estimator_outputs is not None: # Place estimator outputs into a stackable tensor. Note that this # will be replaced with torch.nested.nested_tensor in the future. estimator_outputs_padded = torch.full( @@ -199,11 +199,14 @@ def sample_trajectories( # Increment the step, determine which trajectories are finisihed, and eval # rewards. step += 1 + # new_dones means those trajectories that just finished. Because we + # pad the sink state to every short trajectory, we need to make sure + # to filter out the already done ones. new_dones = ( new_states.is_initial_state if self.estimator.is_backward else sink_states_mask - ) & ~dones # TODO: why is ~dones used here and again later on? Is this intentional? + ) & ~dones trajectories_dones[new_dones & ~dones] = step try: trajectories_log_rewards[new_dones & ~dones] = env.log_reward( diff --git a/src/gfn/states.py b/src/gfn/states.py index 883765b8..416e5670 100644 --- a/src/gfn/states.py +++ b/src/gfn/states.py @@ -1,6 +1,7 @@ from __future__ import annotations # This allows to use the class name in type hints from abc import ABC, abstractmethod +from copy import deepcopy from math import prod from typing import Callable, ClassVar, Optional, Sequence, cast @@ -129,7 +130,7 @@ def __getitem__(self, index: int | Sequence[int] | Sequence[bool]) -> States: """Access particular states of the batch.""" return self.__class__( self.tensor[index] - ) # TODO: Inefficient - this makes a copy of the tensor! + ) # TODO: Inefficient - this might make a copy of the tensor! def __setitem__( self, index: int | Sequence[int] | Sequence[bool], states: States @@ -138,9 +139,8 @@ def __setitem__( self.tensor[index] = states.tensor def clone(self) -> States: - """Returns a clone of the current instance.""" - # TODO: Do we need to copy _log_rewards? - return self.__class__(self.tensor.detach().clone()) + """Returns a *detached* clone of the current instance using deepcopy.""" + return deepcopy(self) def flatten(self) -> States: """Flatten the batch dimension of the states. diff --git a/src/gfn/utils/common.py b/src/gfn/utils/common.py index a80890c5..cc5b97a7 100644 --- a/src/gfn/utils/common.py +++ b/src/gfn/utils/common.py @@ -1,82 +1,17 @@ import random -from collections import Counter -from typing import Dict, Optional import numpy as np import torch -from torchtyping import TensorType as TT -from gfn.containers import Trajectories, Transitions -from gfn.env import Env -from gfn.gflownet import GFlowNet, TBGFlowNet -from gfn.states import States - -def get_terminating_state_dist_pmf(env: Env, states: States) -> TT["n_states", float]: - states_indices = env.get_terminating_states_indices(states).cpu().numpy().tolist() - counter = Counter(states_indices) - counter_list = [ - counter[state_idx] if state_idx in counter else 0 - for state_idx in range(env.n_terminating_states) - ] - - return torch.tensor(counter_list, dtype=torch.float) / len(states_indices) - - -def validate( - env: Env, - gflownet: GFlowNet, - n_validation_samples: int = 1000, - visited_terminating_states: Optional[States] = None, -) -> Dict[str, float]: - """Evaluates the current gflownet on the given environment. - - This is for environments with known target reward. The validation is done by - computing the l1 distance between the learned empirical and the target - distributions. - - Args: - env: The environment to evaluate the gflownet on. - gflownet: The gflownet to evaluate. - n_validation_samples: The number of samples to use to evaluate the pmf. - visited_terminating_states: The terminating states visited during training. If given, the pmf is obtained from - these last n_validation_samples states. Otherwise, n_validation_samples are resampled for evaluation. - - Returns: A dictionary containing the l1 validation metric. If the gflownet - is a TBGFlowNet, i.e. contains LogZ, then the (absolute) difference - between the learned and the target LogZ is also returned in the dictionary. - """ - - true_logZ = env.log_partition - true_dist_pmf = env.true_dist_pmf - if isinstance(true_dist_pmf, torch.Tensor): - true_dist_pmf = true_dist_pmf.cpu() - else: - # The environment does not implement a true_dist_pmf property, nor a log_partition property - # We cannot validate the gflownet - return {} - - logZ = None - if isinstance(gflownet, TBGFlowNet): - logZ = gflownet.logZ.item() - if visited_terminating_states is None: - terminating_states = gflownet.sample_terminating_states(n_validation_samples) - else: - terminating_states = visited_terminating_states[-n_validation_samples:] - - final_states_dist_pmf = get_terminating_state_dist_pmf(env, terminating_states) - l1_dist = (final_states_dist_pmf - true_dist_pmf).abs().mean().item() - validation_info = {"l1_dist": l1_dist} - if logZ is not None: - validation_info["logZ_diff"] = abs(logZ - true_logZ) - return validation_info - - -def set_seed(seed: int) -> None: +def set_seed(seed: int, performance_mode: bool = False) -> None: """Used to control randomness.""" torch.manual_seed(seed) random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) - torch.backends.cudnn.deterministic = True - torch.backends.cudnn.benchmark = False + + # These are only set when we care about reproducibility over performance. + if not performance_mode: + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False diff --git a/src/gfn/utils/modules.py b/src/gfn/utils/modules.py index f99aa22d..2ffbf54a 100644 --- a/src/gfn/utils/modules.py +++ b/src/gfn/utils/modules.py @@ -50,7 +50,7 @@ def __init__( arch.append(nn.Linear(hidden_dim, hidden_dim)) arch.append(activation()) self.torso = nn.Sequential(*arch) - self.torso.hidden_dim = hidden_dim # TODO: what is this? + self.torso.hidden_dim = hidden_dim else: self.torso = torso self.last_layer = nn.Linear(self.torso.hidden_dim, output_dim) diff --git a/src/gfn/utils/training.py b/src/gfn/utils/training.py new file mode 100644 index 00000000..9144154b --- /dev/null +++ b/src/gfn/utils/training.py @@ -0,0 +1,69 @@ +from collections import Counter +from typing import Dict, Optional + +import torch +from torchtyping import TensorType as TT + +from gfn.env import Env +from gfn.gflownet import GFlowNet, TBGFlowNet +from gfn.states import States + + +def get_terminating_state_dist_pmf(env: Env, states: States) -> TT["n_states", float]: + states_indices = env.get_terminating_states_indices(states).cpu().numpy().tolist() + counter = Counter(states_indices) + counter_list = [ + counter[state_idx] if state_idx in counter else 0 + for state_idx in range(env.n_terminating_states) + ] + + return torch.tensor(counter_list, dtype=torch.float) / len(states_indices) + + +def validate( + env: Env, + gflownet: GFlowNet, + n_validation_samples: int = 1000, + visited_terminating_states: Optional[States] = None, +) -> Dict[str, float]: + """Evaluates the current gflownet on the given environment. + + This is for environments with known target reward. The validation is done by + computing the l1 distance between the learned empirical and the target + distributions. + + Args: + env: The environment to evaluate the gflownet on. + gflownet: The gflownet to evaluate. + n_validation_samples: The number of samples to use to evaluate the pmf. + visited_terminating_states: The terminating states visited during training. If given, the pmf is obtained from + these last n_validation_samples states. Otherwise, n_validation_samples are resampled for evaluation. + + Returns: A dictionary containing the l1 validation metric. If the gflownet + is a TBGFlowNet, i.e. contains LogZ, then the (absolute) difference + between the learned and the target LogZ is also returned in the dictionary. + """ + + true_logZ = env.log_partition + true_dist_pmf = env.true_dist_pmf + if isinstance(true_dist_pmf, torch.Tensor): + true_dist_pmf = true_dist_pmf.cpu() + else: + # The environment does not implement a true_dist_pmf property, nor a log_partition property + # We cannot validate the gflownet + return {} + + logZ = None + if isinstance(gflownet, TBGFlowNet): + logZ = gflownet.logZ.item() + if visited_terminating_states is None: + terminating_states = gflownet.sample_terminating_states(n_validation_samples) + else: + terminating_states = visited_terminating_states[-n_validation_samples:] + + final_states_dist_pmf = get_terminating_state_dist_pmf(env, terminating_states) + l1_dist = (final_states_dist_pmf - true_dist_pmf).abs().mean().item() + validation_info = {"l1_dist": l1_dist} + if logZ is not None: + validation_info["logZ_diff"] = abs(logZ - true_logZ) + return validation_info diff --git a/testing/test_parametrizations_and_losses.py b/testing/test_parametrizations_and_losses.py index b33bb9ef..f2e725bf 100644 --- a/testing/test_parametrizations_and_losses.py +++ b/testing/test_parametrizations_and_losses.py @@ -208,7 +208,9 @@ def PFBasedGFlowNet_with_return( else: raise ValueError(f"Unknown gflownet {gflownet_name}") - trajectories = gflownet.sample_trajectories(env, sample_off_policy=False, n_samples=10) + trajectories = gflownet.sample_trajectories( + env, sample_off_policy=False, n_samples=10 + ) training_objects = gflownet.to_training_samples(trajectories) _ = gflownet.loss(env, training_objects) @@ -305,7 +307,9 @@ def test_subTB_vs_TB( zero_logF=True, ) - trajectories = gflownet.sample_trajectories(env, sample_off_policy=False, n_samples=10) + trajectories = gflownet.sample_trajectories( + env, sample_off_policy=False, n_samples=10 + ) subtb_loss = gflownet.loss(env, trajectories) if weighting == "TB": diff --git a/testing/test_samplers_and_trajectories.py b/testing/test_samplers_and_trajectories.py index 77eca901..71bdbc04 100644 --- a/testing/test_samplers_and_trajectories.py +++ b/testing/test_samplers_and_trajectories.py @@ -80,12 +80,16 @@ def trajectory_sampling_with_return( sampler = Sampler(estimator=pf_estimator) # Test mode collects log_probs and estimator_ouputs, not encountered in the wild. - trajectories = sampler.sample_trajectories(env, off_policy=False, n_trajectories=5, test_mode=True) + trajectories = sampler.sample_trajectories( + env, off_policy=False, n_trajectories=5, debug_mode=True + ) # trajectories = sampler.sample_trajectories(env, n_trajectories=10) # TODO - why is this duplicated? states = env.reset(batch_shape=5, random=True) bw_sampler = Sampler(estimator=pb_estimator) - bw_trajectories = bw_sampler.sample_trajectories(env, off_policy=False, states=states) + bw_trajectories = bw_sampler.sample_trajectories( + env, off_policy=False, states=states + ) return trajectories, bw_trajectories, pf_estimator, pb_estimator diff --git a/tutorials/examples/train_box.py b/tutorials/examples/train_box.py index e9ecbeae..5a3cf8dd 100644 --- a/tutorials/examples/train_box.py +++ b/tutorials/examples/train_box.py @@ -6,7 +6,6 @@ python train_box.py --delta {0.1, 0.25} --tied {--uniform_pb} --loss {TB, DB} """ - from argparse import ArgumentParser import numpy as np diff --git a/tutorials/examples/train_discreteebm.py b/tutorials/examples/train_discreteebm.py index 3a441648..45428423 100644 --- a/tutorials/examples/train_discreteebm.py +++ b/tutorials/examples/train_discreteebm.py @@ -10,7 +10,6 @@ [Learning GFlowNets from partial episodes for improved convergence and stability](https://arxiv.org/abs/2209.12782) python train_hypergrid.py --ndim {2, 4} --height 12 --R0 {1e-3, 1e-4} --tied --loss {TB, DB, SubTB} """ - from argparse import ArgumentParser import torch @@ -20,8 +19,14 @@ from gfn.gflownet import FMGFlowNet from gfn.gym import DiscreteEBM from gfn.modules import DiscretePolicyEstimator +<<<<<<< HEAD from gfn.utils.common import set_seed, validate from gfn.utils.modules import NeuralNet, Tabular +======= +from gfn.utils.common import set_seed +from gfn.utils.modules import NeuralNet, Tabular +from gfn.utils.training import validate +>>>>>>> eedc7e8735f6dc4aafc748f149cbae24b47d9062 DEFAULT_SEED = 4444 diff --git a/tutorials/examples/train_hypergrid.py b/tutorials/examples/train_hypergrid.py index 517da98e..f4548af0 100644 --- a/tutorials/examples/train_hypergrid.py +++ b/tutorials/examples/train_hypergrid.py @@ -10,7 +10,6 @@ [Learning GFlowNets from partial episodes for improved convergence and stability](https://arxiv.org/abs/2209.12782) python train_hypergrid.py --ndim {2, 4} --height 12 --R0 {1e-3, 1e-4} --tied --loss {TB, DB, SubTB} """ - from argparse import ArgumentParser import torch @@ -28,8 +27,14 @@ ) from gfn.gym import HyperGrid from gfn.modules import DiscretePolicyEstimator, ScalarEstimator +<<<<<<< HEAD from gfn.utils.common import set_seed, validate from gfn.utils.modules import DiscreteUniform, NeuralNet, Tabular +======= +from gfn.utils.common import set_seed +from gfn.utils.modules import DiscreteUniform, NeuralNet, Tabular +from gfn.utils.training import validate +>>>>>>> eedc7e8735f6dc4aafc748f149cbae24b47d9062 DEFAULT_SEED = 4444 diff --git a/tutorials/examples/train_line.py b/tutorials/examples/train_line.py index ccbbf1cf..db03f684 100644 --- a/tutorials/examples/train_line.py +++ b/tutorials/examples/train_line.py @@ -1,3 +1,8 @@ +<<<<<<< HEAD +======= +from typing import ClassVar, Literal, Tuple + +>>>>>>> eedc7e8735f6dc4aafc748f149cbae24b47d9062 import matplotlib.pyplot as plt import numpy as np import torch