diff --git a/README.md b/README.md index 58eba7c3..395064a9 100644 --- a/README.md +++ b/README.md @@ -71,12 +71,10 @@ from gfn.utils import NeuralNet # NeuralNet is a simple multi-layer perceptron if __name__ == "__main__": - # 1 - We define the environment - - env = HyperGrid(ndim=4, height=8, R0=0.01) # Grid of size 8x8x8x8 - - # 2 - We define the needed modules (neural networks) + # 1 - We define the environment. + env = HyperGrid(ndim=4, height=8, R0=0.01) # Grid of size 8x8x8x8 + # 2 - We define the needed modules (neural networks). # The environment has a preprocessor attribute, which is used to preprocess the state before feeding it to the policy estimator module_PF = NeuralNet( input_dim=env.preprocessor.output_dim, @@ -88,17 +86,14 @@ if __name__ == "__main__": torso=module_PF.torso # We share all the parameters of P_F and P_B, except for the last layer ) - # 3 - We define the estimators - + # 3 - We define the estimators. pf_estimator = DiscretePolicyEstimator(module_PF, env.n_actions, is_backward=False, preprocessor=env.preprocessor) pb_estimator = DiscretePolicyEstimator(module_PB, env.n_actions, is_backward=True, preprocessor=env.preprocessor) - # 4 - We define the GFlowNet - + # 4 - We define the GFlowNet. gfn = TBGFlowNet(init_logZ=0., pf=pf_estimator, pb=pb_estimator) # We initialize logZ to 0 - # 5 - We define the sampler and the optimizer - + # 5 - We define the sampler and the optimizer. sampler = Sampler(estimator=pf_estimator) # We use an on-policy sampler, based on the forward policy # Policy parameters have their own LR. @@ -110,7 +105,6 @@ if __name__ == "__main__": optimizer.add_param_group({"params": logz_params, "lr": 1e-1}) # 6 - We train the GFlowNet for 1000 iterations, with 16 trajectories per iteration - for i in (pbar := tqdm(range(1000))): trajectories = sampler.sample_trajectories(env=env, n_trajectories=16) optimizer.zero_grad() @@ -193,6 +187,8 @@ Training GFlowNets requires one or multiple estimators, called `GFNModule`s, whi For non-discrete environments, the user needs to specify their own policies $P_F$ and $P_B$. The module, taking as input a batch of states (as a `States`) object, should return the batched parameters of a `torch.Distribution`. The distribution depends on the environment. The `to_probability_distribution` function handles the conversion of the parameter outputs to an actual batched `Distribution` object, that implements at least the `sample` and `log_prob` functions. An example is provided [here](https://github.com/saleml/torchgfn/tree/master/src/gfn/gym/helpers/box_utils.py), for a square environment in which the forward policy has support either on a quarter disk, or on an arc-circle, such that the angle, and the radius (for the quarter disk part) are scaled samples from a mixture of Beta distributions. The provided example shows an intricate scenario, and it is not expected that user defined environment need this much level of details. +In general, (and perhaps obviously) the `to_probability_distribution` method is used to calculate a probability distribution from a policy. Therefore, in order to go off-policy, one needs to modify the computations in this method during sampling. One accomplishes this using `policy_kwargs`, a `dict` of kwarg-value pairs which are used by the `Estimator` when calculating the new policy. In the discrete case, where common settings apply, one can see their use in `DiscretePolicyEstimator`'s `to_probability_distribution` method by passing a softmax `temperature`, `sf_bias` (a scalar to subtract from the exit action logit) or `epsilon` which allows for e-greedy style exploration. In the continuous case, it is not possible to forsee the methods used for off-policy exploration (as it depends on the details of the `to_probability_distribution` method, which is not generic for continuous GFNs), so this must be handled by the user, using custom `policy_kwargs`. + In all `GFNModule`s, note that the input of the `forward` function is a `States` object. Meaning that they first need to be transformed to tensors. However, `states.tensor` does not necessarily include the structure that a neural network can used to generalize. It is common in these scenarios to have a function that transforms these raw tensor states to ones where the structure is clearer, via a `Preprocessor` object, that is part of the environment. More on this [here](https://github.com/saleml/torchgfn/tree/master/tutorials/ENV.md). The default preprocessor of an environment is the identity preprocessor. The `forward` pass thus first calls the `preprocessor` attribute of the environment on `States`, before performing any transformation. The `preprocessor` is thus an attribute of the module. If it is not explicitly defined, it is set to the identity preprocessor. For discrete environments, a `Tabular` module is provided, where a lookup table is used instead of a neural network. Additionally, a `UniformPB` module is provided, implementing a uniform backward policy. These modules are provided [here](https://github.com/saleml/torchgfn/tree/master/src/gfn/utils/modules.py). diff --git a/src/gfn/containers/trajectories.py b/src/gfn/containers/trajectories.py index 7b775301..3ca3b47e 100644 --- a/src/gfn/containers/trajectories.py +++ b/src/gfn/containers/trajectories.py @@ -7,13 +7,20 @@ from gfn.env import Env from gfn.states import States +import numpy as np import torch +from torch import Tensor from torchtyping import TensorType as TT from gfn.containers.base import Container from gfn.containers.transitions import Transitions +def is_tensor(t) -> bool: + """Checks whether t is a torch.Tensor instance.""" + return isinstance(t, Tensor) + + # TODO: remove env from this class? class Trajectories(Container): """Container for complete trajectories (starting in $s_0$ and ending in $s_f$). @@ -47,16 +54,21 @@ def __init__( is_backward: bool = False, log_rewards: TT["n_trajectories", torch.float] | None = None, log_probs: TT["max_length", "n_trajectories", torch.float] | None = None, + estimator_outputs: torch.Tensor | None = None, ) -> None: """ Args: env: The environment in which the trajectories are defined. - states: The states of the trajectories. Defaults to None. - actions: The actions of the trajectories. Defaults to None. - when_is_done: The time step at which each trajectory ends. Defaults to None. - is_backward: Whether the trajectories are backward or forward. Defaults to False. - log_rewards: The log_rewards of the trajectories. Defaults to None. - log_probs: The log probabilities of the trajectories' actions. Defaults to None. + states: The states of the trajectories. + actions: The actions of the trajectories. + when_is_done: The time step at which each trajectory ends. + is_backward: Whether the trajectories are backward or forward. + log_rewards: The log_rewards of the trajectories. + log_probs: The log probabilities of the trajectories' actions. + estimator_outputs: When forward sampling off-policy for an n-step + trajectory, n forward passes will be made on some function approximator, + which may need to be re-used (for example, for evaluating PF). To avoid + duplicated effort, the outputs of the forward passes can be stored here. If states is None, then the states are initialized to an empty States object, that can be populated on the fly. If log_rewards is None, then `env.log_reward` @@ -87,6 +99,7 @@ def __init__( if log_probs is not None else torch.full(size=(0, 0), fill_value=0, dtype=torch.float) ) + self.estimator_outputs = estimator_outputs def __repr__(self) -> str: states = self.states.tensor.transpose(0, 1) @@ -154,6 +167,21 @@ def __getitem__(self, index: int | Sequence[int]) -> Trajectories: log_rewards = ( self._log_rewards[index] if self._log_rewards is not None else None ) + 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 return Trajectories( env=self.env, @@ -163,6 +191,7 @@ def __getitem__(self, index: int | Sequence[int]) -> Trajectories: is_backward=self.is_backward, log_rewards=log_rewards, log_probs=log_probs, + estimator_outputs=estimator_outputs, ) @staticmethod @@ -198,7 +227,10 @@ 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) self.when_is_done = torch.cat((self.when_is_done, other.when_is_done), dim=0) @@ -213,11 +245,76 @@ def extend(self, other: Trajectories) -> None: if self._log_rewards is not None and other._log_rewards is not None: self._log_rewards = torch.cat( - (self._log_rewards, other._log_rewards), dim=0 + (self._log_rewards, other._log_rewards), + dim=0, ) else: self._log_rewards = None + # Either set, or append, estimator outputs if they exist in the submitted + # trajectory. + if self.estimator_outputs is None and is_tensor(other.estimator_outputs): + self.estimator_outputs = other.estimator_outputs + elif is_tensor(self.estimator_outputs) and is_tensor(other.estimator_outputs): + batch_shape = self.actions.batch_shape + n_bs = len(batch_shape) + output_dtype = self.estimator_outputs.dtype + + if n_bs == 1: + # Concatenate along the only batch dimension. + self.estimator_outputs = torch.cat( + (self.estimator_outputs, other.estimator_outputs), + dim=0, + ) + elif n_bs == 2: + if self.estimator_outputs.shape[0] != other.estimator_outputs.shape[0]: + # First we need to pad the first dimension on either self or other. + self_shape = np.array(self.estimator_outputs.shape) + 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 (#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] + pad_dim_full = (pad_dim,) + tuple(self_shape[1:]) + output_padding = torch.full( + pad_dim_full, + fill_value=-float("inf"), + dtype=self.estimator_outputs.dtype, # TODO: This isn't working! Hence the cast below... + device=self.estimator_outputs.device, + ) + self.estimator_outputs = torch.cat( + (self.estimator_outputs, output_padding), + dim=0, + ) + + # The size of other needs to grow to match self along dim=0. + if other_shape[0] < self_shape[0]: + pad_dim = required_first_dim - other_shape[0] + pad_dim_full = (pad_dim,) + tuple(other_shape[1:]) + output_padding = torch.full( + pad_dim_full, + fill_value=-float("inf"), + dtype=other.estimator_outputs.dtype, # TODO: This isn't working! Hence the cast below... + device=other.estimator_outputs.device, + ) + other.estimator_outputs = torch.cat( + (other.estimator_outputs, output_padding), + dim=0, + ) + + # Concatenate the tensors along the second dimension. + self.estimator_outputs = torch.cat( + (self.estimator_outputs, other.estimator_outputs), + dim=1, + ).to( + dtype=output_dtype + ) # Cast to prevent single precision becoming double precision... weird. + + # Sanity check. TODO: Remove? + assert self.estimator_outputs.shape[:n_bs] == batch_shape + def to_transitions(self) -> Transitions: """Returns a `Transitions` object from the trajectories.""" states = self.states[:-1][~self.actions.is_dummy] diff --git a/src/gfn/env.py b/src/gfn/env.py index a21ae38d..bf2a3d3b 100644 --- a/src/gfn/env.py +++ b/src/gfn/env.py @@ -1,5 +1,4 @@ from abc import ABC, abstractmethod -from copy import deepcopy from typing import Optional, Tuple, Union import torch @@ -8,6 +7,7 @@ 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,), {}) @@ -23,7 +23,6 @@ def __init__( sf: Optional[TT["state_shape", torch.float]] = None, device_str: Optional[str] = None, preprocessor: Optional[Preprocessor] = None, - log_reward_clip: Optional[float] = -100.0, ): """Initializes an environment. @@ -37,7 +36,6 @@ def __init__( preprocessor: a Preprocessor object that converts raw states to a tensor that can be fed into a neural network. Defaults to None, in which case the IdentityPreprocessor is used. - log_reward_clip: Used to clip small rewards (in particular, log(0) rewards). """ self.device = torch.device(device_str) if device_str is not None else s0.device @@ -58,7 +56,6 @@ def __init__( self.preprocessor = preprocessor self.is_discrete = False - self.log_reward_clip = log_reward_clip @abstractmethod def make_States_class(self) -> type[States]: @@ -83,7 +80,7 @@ def reset( assert not (random and sink) if random and seed is not None: - torch.manual_seed(seed) + set_seed(seed, performance_mode=True) if batch_shape is None: batch_shape = (1,) @@ -94,7 +91,7 @@ def reset( ) @abstractmethod - def maskless_step( + def maskless_step( # TODO: rename to step, other method becomes _step. self, states: States, actions: Actions ) -> TT["batch_shape", "state_shape", torch.float]: """Function that takes a batch of states and actions and returns a batch of next @@ -102,7 +99,7 @@ def maskless_step( """ @abstractmethod - def maskless_backward_step( + def maskless_backward_step( # TODO: rename to backward_step, other method becomes _backward_step. self, states: States, actions: Actions ) -> TT["batch_shape", "state_shape", torch.float]: """Function that takes a batch of states and actions and returns a batch of previous @@ -134,7 +131,7 @@ def step( ) -> States: """Function that takes a batch of states and actions and returns a batch of next states and a boolean tensor indicating sink states in the new batch.""" - new_states = deepcopy(states) + new_states = states.clone() # TODO: Ensure this is efficient! valid_states_idx: TT["batch_shape", torch.bool] = ~states.is_sink_state valid_actions = actions[valid_states_idx] valid_states = states[valid_states_idx] @@ -154,8 +151,6 @@ def step( new_not_done_states_tensor = self.maskless_step( not_done_states, not_done_actions ) - # if isinstance(new_states, DiscreteStates): - # new_not_done_states.masks = self.update_masks(not_done_states, not_done_actions) new_states.tensor[~new_sink_states_idx] = new_not_done_states_tensor @@ -168,7 +163,7 @@ def backward_step( ) -> States: """Function that takes a batch of states and actions and returns a batch of next states and a boolean tensor indicating initial states in the new batch.""" - new_states = deepcopy(states) + new_states = states.clone() # TODO: Ensure this is efficient! valid_states_idx: TT["batch_shape", torch.bool] = ~new_states.is_initial_state valid_actions = actions[valid_states_idx] valid_states = states[valid_states_idx] @@ -197,8 +192,8 @@ def reward(self, final_states: States) -> TT["batch_shape", torch.float]: raise NotImplementedError("Reward function is not implemented.") def log_reward(self, final_states: States) -> TT["batch_shape", torch.float]: - """Calculates the log reward (clipping small rewards).""" - return torch.log(self.reward(final_states)).clip(self.log_reward_clip) + """Calculates the log reward.""" + return torch.log(self.reward(final_states)) @property def log_partition(self) -> float: @@ -224,7 +219,6 @@ def __init__( sf: Optional[TT["state_shape", torch.float]] = None, device_str: Optional[str] = None, preprocessor: Optional[Preprocessor] = None, - log_reward_clip: Optional[float] = -100.0, ): """Initializes a discrete environment. @@ -234,12 +228,10 @@ def __init__( sf: The final state tensor (shared among all trajectories). device_str: String representation of a torch.device. preprocessor: An optional preprocessor for intermediate states. - log_reward_clip: Used to clip small rewards (in particular, log(0) rewards). """ self.n_actions = n_actions - super().__init__(s0, sf, device_str, preprocessor, log_reward_clip) + super().__init__(s0, sf, device_str, preprocessor) self.is_discrete = True - self.log_reward_clip = log_reward_clip def make_Actions_class(self) -> type[Actions]: env = self diff --git a/src/gfn/gflownet/base.py b/src/gfn/gflownet/base.py index 0afbcc37..5e04151d 100644 --- a/src/gfn/gflownet/base.py +++ b/src/gfn/gflownet/base.py @@ -1,8 +1,10 @@ +import math from abc import ABC, abstractmethod from typing import Generic, Tuple, TypeVar, Union import torch import torch.nn as nn +from torch import Tensor from torchtyping import TensorType as TT from gfn.containers import Trajectories @@ -24,12 +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. """ @@ -43,9 +48,15 @@ def sample_terminating_states(self, env: Env, n_samples: int) -> States: Returns: States: sampled terminating states object. """ - trajectories = self.sample_trajectories(env, n_samples) + trajectories = self.sample_trajectories(env, n_samples, sample_off_policy=False) return trajectories.last_states + def logz_named_parameters(self): + return {"logZ": dict(self.named_parameters())["logZ"]} + + def logz_parameters(self): + return [dict(self.named_parameters())["logZ"]] + @abstractmethod def to_training_samples(self, trajectories: Trajectories) -> TrainingSampleType: """Converts trajectories to training samples. The type depends on the GFlowNet.""" @@ -63,17 +74,32 @@ class PFBasedGFlowNet(GFlowNet[TrainingSampleType]): pb: GFNModule """ - def __init__(self, pf: GFNModule, pb: GFNModule, on_policy: bool = False): + def __init__(self, pf: GFNModule, pb: GFNModule, off_policy: bool): super().__init__() self.pf = pf self.pb = pb - self.on_policy = on_policy + self.off_policy = off_policy - def sample_trajectories(self, env: Env, n_samples: int) -> Trajectories: + def sample_trajectories( + self, env: Env, n_samples: int, sample_off_policy: bool, **policy_kwargs + ) -> Trajectories: + """Samples trajectories, optionally with specified policy kwargs.""" sampler = Sampler(estimator=self.pf) - trajectories = sampler.sample_trajectories(env, n_trajectories=n_samples) + trajectories = sampler.sample_trajectories( + env, + n_trajectories=n_samples, + off_policy=sample_off_policy, + **policy_kwargs, + ) + 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( @@ -93,8 +119,13 @@ def get_pfs_and_pbs( the one used to evaluate the loss. Otherwise we can use the logprobs directly from the trajectories. + Note - for off policy exploration, the trajectories submitted to this method + will be sampled off policy. + Args: trajectories: Trajectories to evaluate. + estimator_outputs: Optional stored estimator outputs from previous forward + sampling (encountered, for example, when sampling off policy). fill_value: Value to use for invalid states (i.e. $s_f$ that is added to shorter trajectories). @@ -118,13 +149,23 @@ def get_pfs_and_pbs( if valid_states.batch_shape != tuple(valid_actions.batch_shape): raise AssertionError("Something wrong happening with log_pf evaluations") - if self.on_policy: - log_pf_trajectories = trajectories.log_probs - else: - module_output = self.pf(valid_states) + if self.off_policy: + # We re-use the values calculated in .sample_trajectories(). + if trajectories.estimator_outputs is not None: + estimator_outputs = trajectories.estimator_outputs[ + ~trajectories.actions.is_dummy + ] + else: + raise Exception( + "GFlowNet is off policy, but no estimator_outputs found in Trajectories!" + ) + + # Calculates the log PF of the actions sampled off policy. valid_log_pf_actions = self.pf.to_probability_distribution( - valid_states, module_output - ).log_prob(valid_actions.tensor) + valid_states, estimator_outputs + ).log_prob( + valid_actions.tensor + ) # Using the actions sampled off-policy. log_pf_trajectories = torch.full_like( trajectories.actions.tensor[..., 0], fill_value=fill_value, @@ -132,12 +173,17 @@ def get_pfs_and_pbs( ) log_pf_trajectories[~trajectories.actions.is_dummy] = valid_log_pf_actions + else: + log_pf_trajectories = trajectories.log_probs + non_initial_valid_states = valid_states[~valid_states.is_initial_state] non_exit_valid_actions = valid_actions[~valid_actions.is_exit] - module_output = self.pb(non_initial_valid_states) + # Using all non-initial states, calculate the backward policy, and the logprobs + # of those actions. + estimator_outputs = self.pb(non_initial_valid_states) valid_log_pb_actions = self.pb.to_probability_distribution( - non_initial_valid_states, module_output + non_initial_valid_states, estimator_outputs ).log_prob(non_exit_valid_actions.tensor) log_pb_trajectories = torch.full_like( @@ -167,7 +213,11 @@ def get_trajectories_scores( total_log_pf_trajectories = log_pf_trajectories.sum(dim=0) total_log_pb_trajectories = log_pb_trajectories.sum(dim=0) - log_rewards = trajectories.log_rewards.clamp_min(self.log_reward_clip_min) # type: ignore + log_rewards = trajectories.log_rewards + # 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( torch.isinf(total_log_pb_trajectories) ): diff --git a/src/gfn/gflownet/detailed_balance.py b/src/gfn/gflownet/detailed_balance.py index 565a33e8..4cb4e6e2 100644 --- a/src/gfn/gflownet/detailed_balance.py +++ b/src/gfn/gflownet/detailed_balance.py @@ -1,3 +1,4 @@ +import math from typing import Tuple import torch @@ -22,8 +23,9 @@ class DBGFlowNet(PFBasedGFlowNet[Transitions]): Attributes: logF: a ScalarEstimator instance. - on_policy: boolean indicating whether we need to reevaluate the log probs. + off_policy: If true, we need to reevaluate the log probs. forward_looking: whether to implement the forward looking GFN loss. + log_reward_clip_min: If finite, clips log rewards to this value. """ def __init__( @@ -31,12 +33,14 @@ def __init__( pf: GFNModule, pb: GFNModule, logF: ScalarEstimator, - on_policy: bool = False, + off_policy: bool, forward_looking: bool = False, + log_reward_clip_min: float = -float("inf"), ): - super().__init__(pf, pb, on_policy=on_policy) + super().__init__(pf, pb, off_policy=off_policy) self.logF = logF self.forward_looking = forward_looking + self.log_reward_clip_min = log_reward_clip_min def get_scores( self, env: Env, transitions: Transitions @@ -64,17 +68,25 @@ def get_scores( if states.batch_shape != tuple(actions.batch_shape): raise ValueError("Something wrong happening with log_pf evaluations") - if self.on_policy: + if not self.off_policy: valid_log_pf_actions = transitions.log_probs else: - module_output = self.pf(states) + # 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 - ).log_prob(actions.tensor) + ).log_prob( + actions.tensor + ) # Actions sampled off policy. valid_log_F_s = self.logF(states).squeeze(-1) if self.forward_looking: - log_rewards = env.log_reward(states) # RM unsqueeze(-1) + log_rewards = env.log_reward(states) # TODO: RM unsqueeze(-1) ? + 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 @@ -154,9 +166,10 @@ 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.on_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. valid_log_pf_actions = pf_dist.log_prob(actions.tensor) valid_log_pf_s_exit = pf_dist.log_prob( torch.full_like(actions.tensor, actions.__class__.exit_action[0]) diff --git a/src/gfn/gflownet/flow_matching.py b/src/gfn/gflownet/flow_matching.py index bda003bb..a2acae34 100644 --- a/src/gfn/gflownet/flow_matching.py +++ b/src/gfn/gflownet/flow_matching.py @@ -1,4 +1,4 @@ -from typing import Tuple +from typing import Optional, Tuple import torch from torchtyping import TensorType as TT @@ -33,17 +33,31 @@ def __init__(self, logF: DiscretePolicyEstimator, alpha: float = 1.0): self.logF = logF self.alpha = alpha - def sample_trajectories(self, env: Env, n_samples: int = 1000) -> Trajectories: + def sample_trajectories( + self, + env: Env, + off_policy: bool, + n_samples: int = 1000, + **policy_kwargs: Optional[dict], + ) -> Trajectories: + """Sample trajectory with optional kwargs controling the policy.""" if not env.is_discrete: raise NotImplementedError( "Flow Matching GFlowNet only supports discrete environments for now." ) sampler = Sampler(estimator=self.logF) - trajectories = sampler.sample_trajectories(env, n_trajectories=n_samples) + trajectories = sampler.sample_trajectories( + env, + n_trajectories=n_samples, + off_policy=off_policy, + **policy_kwargs, + ) return trajectories def flow_matching_loss( - self, env: Env, states: DiscreteStates + self, + env: Env, + states: DiscreteStates, ) -> TT["n_trajectories", torch.float]: """Computes the FM for the provided states. diff --git a/src/gfn/gflownet/sub_trajectory_balance.py b/src/gfn/gflownet/sub_trajectory_balance.py index e6814c75..f07835c3 100644 --- a/src/gfn/gflownet/sub_trajectory_balance.py +++ b/src/gfn/gflownet/sub_trajectory_balance.py @@ -1,3 +1,4 @@ +import math from typing import List, Literal, Tuple import torch @@ -47,7 +48,7 @@ class SubTBGFlowNet(TrajectoryBasedGFlowNet): proportionally to (lamda ** len(sub_trajectory)), within the set of all sub-trajectories. lamda: discount factor for longer trajectories. - log_reward_clip_min: minimum value for log rewards. + log_reward_clip_min: If finite, clips log rewards to this value. """ def __init__( @@ -55,7 +56,7 @@ def __init__( pf: GFNModule, pb: GFNModule, logF: ScalarEstimator, - on_policy: bool = False, + off_policy: bool, weighting: Literal[ "DB", "ModifiedDB", @@ -66,10 +67,10 @@ def __init__( "equal_within", ] = "geometric_within", lamda: float = 0.9, - log_reward_clip_min: float = -12, # roughly log(1e-5) + log_reward_clip_min: float = -float("inf"), forward_looking: bool = False, ): - super().__init__(pf, pb, on_policy=on_policy) + super().__init__(pf, pb, off_policy=off_policy) self.logF = logF self.weighting = weighting self.lamda = lamda @@ -136,9 +137,11 @@ def calculate_targets( """ targets = torch.full_like(preds, fill_value=-float("inf")) assert trajectories.log_rewards is not None - log_rewards = trajectories.log_rewards[ - trajectories.when_is_done >= i - ].clamp_min(self.log_reward_clip_min) + log_rewards = trajectories.log_rewards[trajectories.when_is_done >= i] + + if math.isfinite(self.log_reward_clip_min): + log_rewards.clamp_min(self.log_reward_clip_min) + targets.T[is_terminal_mask[i - 1 :].T] = log_rewards # For now, the targets contain the log-rewards of the ending sub trajectories @@ -249,6 +252,7 @@ def get_scores( full_mask, i, ) + flattening_mask = trajectories.when_is_done.lt( torch.arange( i, @@ -268,10 +272,7 @@ def get_scores( flattening_masks.append(flattening_mask) scores.append(preds - targets) - return ( - scores, - flattening_masks, - ) + return (scores, flattening_masks) def get_equal_within_contributions( self, trajectories: Trajectories diff --git a/src/gfn/gflownet/trajectory_balance.py b/src/gfn/gflownet/trajectory_balance.py index bceac033..dde1b667 100644 --- a/src/gfn/gflownet/trajectory_balance.py +++ b/src/gfn/gflownet/trajectory_balance.py @@ -23,22 +23,24 @@ class TBGFlowNet(TrajectoryBasedGFlowNet): the DAG, or a singleton thereof, if self.logit_PB is a fixed DiscretePBEstimator. Attributes: + off_policy: Whether the GFlowNet samples trajectories on or off policy. logZ: a LogZEstimator instance. - log_reward_clip_min: minimal value to clamp the reward to. - + log_reward_clip_min: If finite, clips log rewards to this value. """ def __init__( self, pf: GFNModule, pb: GFNModule, - on_policy: bool = False, + off_policy: bool, init_logZ: float = 0.0, - log_reward_clip_min: float = -12, # roughly log(1e-5) + log_reward_clip_min: float = -float("inf"), ): - super().__init__(pf, pb, on_policy=on_policy) + super().__init__(pf, pb, off_policy=off_policy) - self.logZ = nn.Parameter(torch.tensor(init_logZ)) + self.logZ = nn.Parameter( + torch.tensor(init_logZ) + ) # TODO: Optionally, this should be a nn.Module to support conditional GFNs. self.log_reward_clip_min = log_reward_clip_min def loss(self, env: Env, trajectories: Trajectories) -> TT[0, float]: @@ -63,7 +65,8 @@ class LogPartitionVarianceGFlowNet(TrajectoryBasedGFlowNet): """Dataclass which holds the logZ estimate for the Log Partition Variance loss. Attributes: - log_reward_clip_min: minimal value to clamp the reward to. + off_policy: Whether the GFlowNet samples trajectories on or off policy. + log_reward_clip_min: If finite, clips log rewards to this value. Raises: ValueError: if the loss is NaN. @@ -73,14 +76,17 @@ def __init__( self, pf: GFNModule, pb: GFNModule, - on_policy: bool = False, - log_reward_clip_min: float = -12, + off_policy: bool, + log_reward_clip_min: float = -float("inf"), ): - super().__init__(pf, pb, on_policy=on_policy) - - self.log_reward_clip_min = log_reward_clip_min # -12 is roughly log(1e-5) + super().__init__(pf, pb, off_policy=off_policy) + self.log_reward_clip_min = log_reward_clip_min - def loss(self, env: Env, trajectories: Trajectories) -> TT[0, float]: + def loss( + self, + env: Env, + trajectories: Trajectories, + ) -> TT[0, float]: """Log Partition Variance loss. This method is described in section 3.2 of diff --git a/src/gfn/gym/box.py b/src/gfn/gym/box.py index 5aa272a7..d5a899bd 100644 --- a/src/gfn/gym/box.py +++ b/src/gfn/gym/box.py @@ -20,7 +20,6 @@ def __init__( R2: float = 2.0, epsilon: float = 1e-4, device_str: Literal["cpu", "cuda"] = "cpu", - log_reward_clip: float = -100.0, ): assert 0 < delta <= 1, "delta must be in (0, 1]" self.delta = delta @@ -31,7 +30,7 @@ def __init__( self.R1 = R1 self.R2 = R2 - super().__init__(s0=s0, log_reward_clip=log_reward_clip) + super().__init__(s0=s0) def make_States_class(self) -> type[States]: env = self diff --git a/src/gfn/gym/discrete_ebm.py b/src/gfn/gym/discrete_ebm.py index ea73b336..a4f82735 100644 --- a/src/gfn/gym/discrete_ebm.py +++ b/src/gfn/gym/discrete_ebm.py @@ -48,7 +48,6 @@ def __init__( alpha: float = 1.0, device_str: Literal["cpu", "cuda"] = "cpu", preprocessor_name: Literal["Identity", "Enum"] = "Identity", - log_reward_clip: float = -100.0, ): """Discrete EBM environment. @@ -60,7 +59,6 @@ def __init__( device_str: "cpu" or "cuda". Defaults to "cpu". preprocessor_name: "KHot" or "OneHot" or "Identity". Defaults to "KHot". - log_reward_clip: Minimum log reward allowable (namely, for log(0)). """ self.ndim = ndim @@ -94,7 +92,6 @@ def __init__( sf=sf, device_str=device_str, preprocessor=preprocessor, - log_reward_clip=log_reward_clip, ) def make_States_class(self) -> type[DiscreteStates]: @@ -119,25 +116,6 @@ def make_random_states_tensor( device=env.device, ) - def make_masks( - self, - ) -> Tuple[ - TT["batch_shape", "n_actions", torch.bool], - TT["batch_shape", "n_actions - 1", torch.bool], - ]: - forward_masks = torch.zeros( - self.batch_shape + (env.n_actions,), - device=env.device, - dtype=torch.bool, - ) - backward_masks = torch.zeros( - self.batch_shape + (env.n_actions - 1,), - device=env.device, - dtype=torch.bool, - ) - - return forward_masks, backward_masks - def update_masks(self) -> None: self.set_default_typing() self.forward_masks[..., : env.ndim] = self.tensor == -1 @@ -195,7 +173,7 @@ def log_reward(self, final_states: DiscreteStates) -> TT["batch_shape"]: canonical = 2 * raw_states - 1 log_reward = -self.alpha * self.energy(canonical) - return log_reward.clip(self.log_reward_clip) + return log_reward def get_states_indices(self, states: DiscreteStates) -> TT["batch_shape"]: """The chosen encoding is the following: -1 -> 0, 0 -> 1, 1 -> 2, then we convert to base 3""" diff --git a/src/gfn/gym/hypergrid.py b/src/gfn/gym/hypergrid.py index 028f716a..71d2862e 100644 --- a/src/gfn/gym/hypergrid.py +++ b/src/gfn/gym/hypergrid.py @@ -25,7 +25,6 @@ def __init__( reward_cos: bool = False, device_str: Literal["cpu", "cuda"] = "cpu", preprocessor_name: Literal["KHot", "OneHot", "Identity", "Enum"] = "KHot", - log_reward_clip: float = -100.0, ): """HyperGrid environment from the GFlowNets paper. The states are represented as 1-d tensors of length `ndim` with values in @@ -42,7 +41,6 @@ def __init__( reward_cos (bool, optional): Which version of the reward to use. Defaults to False. device_str (str, optional): "cpu" or "cuda". Defaults to "cpu". preprocessor_name (str, optional): "KHot" or "OneHot" or "Identity". Defaults to "KHot". - log_reward_clip: Minimum log reward allowable (namely, for log(0)). """ self.ndim = ndim self.height = height @@ -82,7 +80,6 @@ def __init__( sf=sf, device_str=device_str, preprocessor=preprocessor, - log_reward_clip=log_reward_clip, ) def make_States_class(self) -> type[DiscreteStates]: diff --git a/src/gfn/modules.py b/src/gfn/modules.py index 13080c7f..846ae6d1 100644 --- a/src/gfn/modules.py +++ b/src/gfn/modules.py @@ -1,4 +1,5 @@ from abc import ABC, abstractmethod +from typing import Optional import torch import torch.nn as nn @@ -102,7 +103,7 @@ def to_probability_distribution( self, states: States, module_output: TT["batch_shape", "output_dim", float], - *args, + **policy_kwargs: Optional[dict], ) -> Distribution: """Transform the output of the module into a probability distribution. @@ -169,6 +170,8 @@ def to_probability_distribution( ) -> Categorical: """Returns a probability distribution given a batch of states and module output. + We handle off-policyness using these kwargs. + Args: temperature: scalar to divide the logits by before softmax. Does nothing if set to 1.0 (default), in which case it's on policy. diff --git a/src/gfn/preprocessors.py b/src/gfn/preprocessors.py index b00020dd..c980168d 100644 --- a/src/gfn/preprocessors.py +++ b/src/gfn/preprocessors.py @@ -31,7 +31,9 @@ class IdentityPreprocessor(Preprocessor): This is the default preprocessor used.""" def preprocess(self, states: States) -> TT["batch_shape", "input_dim"]: - return states.tensor.float() + return ( + states.tensor.float() + ) # TODO: should we typecast here? not a true identity... class EnumPreprocessor(Preprocessor): diff --git a/src/gfn/samplers.py b/src/gfn/samplers.py index 83d98221..56cd83de 100644 --- a/src/gfn/samplers.py +++ b/src/gfn/samplers.py @@ -18,27 +18,43 @@ class Sampler: Attributes: estimator: the submitted PolicyEstimator. - probability_distribution_kwargs: keyword arguments to be passed to the `to_probability_distribution` - method of the estimator. For example, for DiscretePolicyEstimators, the kwargs can contain - the `temperature` parameter, `epsilon`, and `sf_bias`. """ def __init__( self, estimator: GFNModule, - **probability_distribution_kwargs: Optional[dict], ) -> None: self.estimator = estimator - self.probability_distribution_kwargs = probability_distribution_kwargs def sample_actions( - self, env: Env, states: States + self, + env: Env, + states: States, + save_estimator_outputs: bool = False, + calculate_logprobs: bool = True, + **policy_kwargs: Optional[dict], ) -> Tuple[Actions, TT["batch_shape", torch.float]]: """Samples actions from the given states. Args: + estimator: A GFNModule to pass to the probability distribution calculator. env: The environment to sample actions from. - states (States): A batch of states. + states: A batch of states. + save_estimator_outputs: If True, the estimator outputs will be returned. + calculate_logprobs: If True, calculates the log probabilities of sampled + actions. + policy_kwargs: keyword arguments to be passed to the + `to_probability_distribution` method of the estimator. For example, for + DiscretePolicyEstimators, the kwargs can contain the `temperature` + 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. + + When sampling off policy, ensure to `save_estimator_outputs` and not + `calculate logprobs`. Log probabilities are instead calculated during the + computation of `PF` as part of the `GFlowNet` class, and the estimator + outputs are required for estimating the logprobs of these off policy + actions. Returns: A tuple of tensors containing: @@ -47,33 +63,54 @@ def sample_actions( the sampled actions under the probability distribution of the given states. """ - module_output = self.estimator(states) + estimator_output = self.estimator(states) dist = self.estimator.to_probability_distribution( - states, module_output, **self.probability_distribution_kwargs + states, estimator_output, **policy_kwargs ) with torch.no_grad(): actions = dist.sample() - log_probs = dist.log_prob(actions) - if torch.any(torch.isinf(log_probs)): - raise RuntimeError("Log probabilities are inf. This should not happen.") - return env.Actions(actions), log_probs + if calculate_logprobs: + log_probs = dist.log_prob(actions) + if torch.any(torch.isinf(log_probs)): + raise RuntimeError("Log probabilities are inf. This should not happen.") + else: + log_probs = None + + actions = env.Actions(actions) + + if not save_estimator_outputs: + estimator_output = None + + return actions, log_probs, estimator_output def sample_trajectories( self, env: Env, + off_policy: bool, states: Optional[States] = None, n_trajectories: Optional[int] = None, + debug_mode: bool = False, + **policy_kwargs, ) -> Trajectories: """Sample trajectories sequentially. Args: env: The environment to sample trajectories from. + off_policy: If True, samples actions such that we skip log probability + calculation, and we save the estimator outputs for later use. states: If given, trajectories would start from such states. Otherwise, trajectories are sampled from $s_o$ and n_trajectories must be provided. n_trajectories: If given, a batch of n_trajectories will be sampled all starting from the environment's s_0. + policy_kwargs: keyword arguments to be passed to the + `to_probability_distribution` method of the estimator. For example, for + DiscretePolicyEstimators, the kwargs can contain the `temperature` + 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. + debug_mode: if True, everything gets calculated. Returns: A Trajectories object representing the batch of sampled trajectories. @@ -81,6 +118,9 @@ def sample_trajectories( AssertionError: When both states and n_trajectories are specified. AssertionError: When states are not linear. """ + save_estimator_outputs = off_policy or debug_mode + skip_logprob_calculaion = off_policy and not debug_mode + if states is None: assert ( n_trajectories is not None @@ -113,15 +153,43 @@ def sample_trajectories( ) step = 0 + all_estimator_outputs = [] while not all(dones): - actions = env.Actions.make_dummy_actions(batch_shape=(n_trajectories,)) + actions = env.Actions.make_dummy_actions( + batch_shape=(n_trajectories,) + ) # TODO: Why do we need this? log_probs = torch.full( (n_trajectories,), fill_value=0, dtype=torch.float, device=device ) - valid_actions, actions_log_probs = self.sample_actions(env, states[~dones]) + # This optionally allows you to retrieve the estimator_outputs collected + # during sampling. This is useful if, for example, you want to evaluate off + # policy actions later without repeating calculations to obtain the env + # distribution parameters. + valid_actions, actions_log_probs, estimator_outputs = self.sample_actions( + env, + states[~dones], + save_estimator_outputs=True if save_estimator_outputs else False, + calculate_logprobs=False if skip_logprob_calculaion else True, + **policy_kwargs, + ) + 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( + (n_trajectories,) + estimator_outputs.shape[1:], + fill_value=-float("inf"), + dtype=torch.float, + device=device, + ) + estimator_outputs_padded[~dones] = estimator_outputs + all_estimator_outputs.append(estimator_outputs_padded) + actions[~dones] = valid_actions - log_probs[~dones] = actions_log_probs + if ( + not skip_logprob_calculaion + ): # When off_policy, actions_log_probs are None. + log_probs[~dones] = actions_log_probs trajectories_actions += [actions] trajectories_logprobs += [log_probs] @@ -131,8 +199,12 @@ def sample_trajectories( new_states = env.step(states, actions) sink_states_mask = new_states.is_sink_state + # 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 @@ -157,6 +229,10 @@ def sample_trajectories( trajectories_actions = env.Actions.stack(trajectories_actions) trajectories_logprobs = torch.stack(trajectories_logprobs, dim=0) + # TODO: use torch.nested.nested_tensor(dtype, device, requires_grad). + if save_estimator_outputs: + all_estimator_outputs = torch.stack(all_estimator_outputs, dim=0) + trajectories = Trajectories( env=env, states=trajectories_states, @@ -165,6 +241,7 @@ def sample_trajectories( is_backward=self.estimator.is_backward, log_rewards=trajectories_log_rewards, log_probs=trajectories_logprobs, + estimator_outputs=all_estimator_outputs if save_estimator_outputs else None, ) return trajectories diff --git a/src/gfn/states.py b/src/gfn/states.py index 0b631f24..e50b6aea 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 ClassVar, Optional, Sequence, cast @@ -131,7 +132,9 @@ def device(self) -> torch.device: def __getitem__(self, index: int | Sequence[int] | Sequence[bool]) -> States: """Access particular states of the batch.""" - return self.__class__(self.tensor[index]) + return self.__class__( + self.tensor[index] + ) # TODO: Inefficient - this might make a copy of the tensor! def __setitem__( self, index: int | Sequence[int] | Sequence[bool], states: States @@ -139,6 +142,10 @@ def __setitem__( """Set particular states of the batch.""" self.tensor[index] = states.tensor + def clone(self) -> States: + """Returns a *detached* clone of the current instance using deepcopy.""" + return deepcopy(self) + def flatten(self) -> States: """Flatten the batch dimension of the states. @@ -241,6 +248,7 @@ def is_initial_state(self) -> TT["batch_shape", torch.bool]: @property def is_sink_state(self) -> TT["batch_shape", torch.bool]: """Return a tensor that is True for states that are $s_f$ of the DAG.""" + # TODO: self.__class__.sf == self.tensor -- or something similar? sink_states = self.__class__.sf.repeat( *self.batch_shape, *((1,) * len(self.__class__.state_shape)) ).to(self.tensor.device) @@ -287,17 +295,17 @@ def __init__( """ super().__init__(tensor) - self.forward_masks = torch.ones( - (*self.batch_shape, self.__class__.n_actions), - dtype=torch.bool, - device=self.__class__.device, - ) - self.backward_masks = torch.ones( - (*self.batch_shape, self.__class__.n_actions - 1), - dtype=torch.bool, - device=self.__class__.device, - ) if forward_masks is None and backward_masks is None: + self.forward_masks = torch.ones( + (*self.batch_shape, self.__class__.n_actions), + dtype=torch.bool, + device=self.__class__.device, + ) + self.backward_masks = torch.ones( + (*self.batch_shape, self.__class__.n_actions - 1), + dtype=torch.bool, + device=self.__class__.device, + ) self.update_masks() else: self.forward_masks = cast(torch.Tensor, forward_masks) @@ -305,6 +313,14 @@ def __init__( self.set_default_typing() + def clone(self) -> States: + """Returns a clone of the current instance.""" + return self.__class__( + self.tensor.detach().clone(), + self.forward_masks, + self.backward_masks, + ) + def set_default_typing(self) -> None: """A convienience function for default typing of the masks.""" self.forward_masks = cast( diff --git a/src/gfn/utils/common.py b/src/gfn/utils/common.py index 1b7cd274..cc5b97a7 100644 --- a/src/gfn/utils/common.py +++ b/src/gfn/utils/common.py @@ -1,70 +1,17 @@ -from collections import Counter -from typing import Dict, Optional +import random +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 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) -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 + # 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 7379d276..2ffbf54a 100644 --- a/src/gfn/utils/modules.py +++ b/src/gfn/utils/modules.py @@ -45,11 +45,11 @@ def __init__( activation = nn.ReLU elif activation_fn == "tanh": activation = nn.Tanh - self.torso = [nn.Linear(input_dim, hidden_dim), activation()] + arch = [nn.Linear(input_dim, hidden_dim), activation()] for _ in range(n_hidden_layers - 1): - self.torso.append(nn.Linear(hidden_dim, hidden_dim)) - self.torso.append(activation()) - self.torso = nn.Sequential(*self.torso) + arch.append(nn.Linear(hidden_dim, hidden_dim)) + arch.append(activation()) + self.torso = nn.Sequential(*arch) self.torso.hidden_dim = hidden_dim else: self.torso = torso @@ -68,7 +68,9 @@ def forward( """ if self.device is None: self.device = preprocessed_states.device - self.to(self.device) + self.to( + self.device + ) # TODO: This is maybe fine but could result in weird errors if the model keeps bouncing between devices. out = self.torso(preprocessed_states) out = self.last_layer(out) return out 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_gflownet.py b/testing/test_gflownet.py index 5e545f46..35642020 100644 --- a/testing/test_gflownet.py +++ b/testing/test_gflownet.py @@ -27,7 +27,7 @@ def test_trajectory_based_gflownet_generic(): ) pb_estimator = BoxPBEstimator(env=env, module=pb_module, n_components=1) - gflownet = TBGFlowNet(pf=pf_estimator, pb=pb_estimator) + gflownet = TBGFlowNet(pf=pf_estimator, pb=pb_estimator, off_policy=False) mock_trajectories = Trajectories(env) result = gflownet.to_training_samples(mock_trajectories) @@ -79,7 +79,7 @@ def test_pytorch_inheritance(): ) pb_estimator = BoxPBEstimator(env=env, module=pb_module, n_components=1) - tbgflownet = TBGFlowNet(pf=pf_estimator, pb=pb_estimator) + tbgflownet = TBGFlowNet(pf=pf_estimator, pb=pb_estimator, off_policy=False) assert hasattr( tbgflownet.parameters(), "__iter__" ), "Expected gflownet to have iterable parameters() method inherited from nn.Module" diff --git a/testing/test_parametrizations_and_losses.py b/testing/test_parametrizations_and_losses.py index ac7ffb5d..f2e725bf 100644 --- a/testing/test_parametrizations_and_losses.py +++ b/testing/test_parametrizations_and_losses.py @@ -57,7 +57,7 @@ def test_FM(env_name: int, ndim: int, module_name: str): ) gflownet = FMGFlowNet(log_F_edge) # forward looking by default. - trajectories = gflownet.sample_trajectories(env, n_samples=10) + trajectories = gflownet.sample_trajectories(env, off_policy=False, n_samples=10) states_tuple = trajectories.to_non_initial_intermediary_and_terminating_states() loss = gflownet.loss(env, states_tuple) assert loss >= 0 @@ -71,8 +71,8 @@ def test_get_pfs_and_pbs(env_name: str, preprocessor_name: str): trajectories, _, pf_estimator, pb_estimator = trajectory_sampling_with_return( env_name, preprocessor_name, delta=0.1, n_components=1, n_components_s0=1 ) - gflownet_on = TBGFlowNet(pf=pf_estimator, pb=pb_estimator, on_policy=True) - gflownet_off = TBGFlowNet(pf=pf_estimator, pb=pb_estimator, on_policy=False) + gflownet_on = TBGFlowNet(pf=pf_estimator, pb=pb_estimator, off_policy=False) + gflownet_off = TBGFlowNet(pf=pf_estimator, pb=pb_estimator, off_policy=True) log_pfs_on, log_pbs_on = gflownet_on.get_pfs_and_pbs(trajectories) log_pfs_off, log_pbs_off = gflownet_off.get_pfs_and_pbs(trajectories) @@ -86,8 +86,8 @@ def test_get_scores(env_name: str, preprocessor_name: str): trajectories, _, pf_estimator, pb_estimator = trajectory_sampling_with_return( env_name, preprocessor_name, delta=0.1, n_components=1, n_components_s0=1 ) - gflownet_on = TBGFlowNet(pf=pf_estimator, pb=pb_estimator, on_policy=True) - gflownet_off = TBGFlowNet(pf=pf_estimator, pb=pb_estimator, on_policy=False) + gflownet_on = TBGFlowNet(pf=pf_estimator, pb=pb_estimator, off_policy=False) + gflownet_off = TBGFlowNet(pf=pf_estimator, pb=pb_estimator, off_policy=True) scores_on = gflownet_on.get_trajectories_scores(trajectories) scores_off = gflownet_off.get_trajectories_scores(trajectories) assert all( @@ -189,24 +189,28 @@ def PFBasedGFlowNet_with_return( forward_looking=forward_looking, pf=pf, pb=pb, + off_policy=False, ) elif gflownet_name == "ModifiedDB": - gflownet = ModifiedDBGFlowNet(pf=pf, pb=pb) + gflownet = ModifiedDBGFlowNet(pf=pf, pb=pb, off_policy=False) elif gflownet_name == "TB": - gflownet = TBGFlowNet(pf=pf, pb=pb) + gflownet = TBGFlowNet(pf=pf, pb=pb, off_policy=False) elif gflownet_name == "ZVar": - gflownet = LogPartitionVarianceGFlowNet(pf=pf, pb=pb) + gflownet = LogPartitionVarianceGFlowNet(pf=pf, pb=pb, off_policy=False) elif gflownet_name == "SubTB": gflownet = SubTBGFlowNet( logF=logF, weighting=sub_tb_weighting, pf=pf, pb=pb, + off_policy=False, ) else: raise ValueError(f"Unknown gflownet {gflownet_name}") - trajectories = gflownet.sample_trajectories(env, 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) @@ -303,11 +307,13 @@ def test_subTB_vs_TB( zero_logF=True, ) - trajectories = gflownet.sample_trajectories(env, 10) + trajectories = gflownet.sample_trajectories( + env, sample_off_policy=False, n_samples=10 + ) subtb_loss = gflownet.loss(env, trajectories) if weighting == "TB": - tb_loss = TBGFlowNet(pf=pf, pb=pb).loss( + tb_loss = TBGFlowNet(pf=pf, pb=pb, off_policy=False).loss( env, trajectories ) # LogZ is default 0.0. assert (tb_loss - subtb_loss).abs() < 1e-4 diff --git a/testing/test_samplers_and_trajectories.py b/testing/test_samplers_and_trajectories.py index a871d940..71bdbc04 100644 --- a/testing/test_samplers_and_trajectories.py +++ b/testing/test_samplers_and_trajectories.py @@ -79,12 +79,17 @@ def trajectory_sampling_with_return( ) sampler = Sampler(estimator=pf_estimator) - trajectories = sampler.sample_trajectories(env, n_trajectories=5) - trajectories = sampler.sample_trajectories(env, n_trajectories=10) + # Test mode collects log_probs and estimator_ouputs, not encountered in the wild. + 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, 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/test_scripts.py b/tutorials/examples/test_scripts.py index 0e2021ec..ae592a97 100644 --- a/tutorials/examples/test_scripts.py +++ b/tutorials/examples/test_scripts.py @@ -5,8 +5,8 @@ from dataclasses import dataclass -import pytest import numpy as np +import pytest from .train_box import main as train_box_main from .train_discreteebm import main as train_discreteebm_main @@ -69,13 +69,13 @@ def test_hypergrid(ndim: int, height: int): args = HypergridArgs(ndim=ndim, height=height, n_trajectories=n_trajectories) final_l1_dist = train_hypergrid_main(args) if ndim == 2 and height == 8: - assert np.isclose(final_l1_dist, 9.14e-4, atol=1e-5) + assert np.isclose(final_l1_dist, 8.78e-4, atol=1e-4) elif ndim == 2 and height == 16: - assert np.isclose(final_l1_dist, 4.56e-4, atol=1e-5) + assert np.isclose(final_l1_dist, 4.56e-4, atol=1e-4) elif ndim == 4 and height == 8: - assert np.isclose(final_l1_dist, 1.6e-4, atol=1e-5) + assert np.isclose(final_l1_dist, 1.6e-4, atol=1e-4) elif ndim == 4 and height == 16: - assert np.isclose(final_l1_dist, 2.45e-5, atol=1e-6) + assert np.isclose(final_l1_dist, 2.45e-5, atol=1e-5) @pytest.mark.parametrize("ndim", [2, 4]) @@ -85,13 +85,13 @@ def test_discreteebm(ndim: int, alpha: float): args = DiscreteEBMArgs(ndim=ndim, alpha=alpha, n_trajectories=n_trajectories) final_l1_dist = train_discreteebm_main(args) if ndim == 2 and alpha == 0.1: - assert np.isclose(final_l1_dist, 2.97e-3, atol=1e-3) + assert np.isclose(final_l1_dist, 2.97e-3, atol=1e-2) elif ndim == 2 and alpha == 1.0: - assert np.isclose(final_l1_dist, 0.017, atol=1e-3) + assert np.isclose(final_l1_dist, 0.017, atol=1e-2) elif ndim == 4 and alpha == 0.1: - assert np.isclose(final_l1_dist, 0.009, atol=1e-3) + assert np.isclose(final_l1_dist, 0.009, atol=1e-2) elif ndim == 4 and alpha == 1.0: - assert np.isclose(final_l1_dist, 0.062, atol=1e-3) + assert np.isclose(final_l1_dist, 0.062, atol=1e-2) @pytest.mark.parametrize("delta", [0.1, 0.25]) @@ -114,10 +114,10 @@ def test_box(delta: float, loss: str): print(args) final_jsd = train_box_main(args) if loss == "TB" and delta == 0.1: - assert np.isclose(final_jsd, 3.81e-2, atol=1e-3) + assert np.isclose(final_jsd, 3.81e-2, atol=1e-2) elif loss == "DB" and delta == 0.1: - assert np.isclose(final_jsd, 0.134, atol=1e-2) + assert np.isclose(final_jsd, 0.134, atol=1e-1) if loss == "TB" and delta == 0.25: - assert np.isclose(final_jsd, 2.93e-3, atol=1e-3) + assert np.isclose(final_jsd, 0.0411, atol=1e-1) elif loss == "DB" and delta == 0.25: - assert np.isclose(final_jsd, 0.0142, atol=1e-3) + assert np.isclose(final_jsd, 0.0142, atol=1e-2) diff --git a/tutorials/examples/train_box.py b/tutorials/examples/train_box.py index 7483fecf..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 @@ -32,6 +31,7 @@ BoxStateFlowModule, ) from gfn.modules import ScalarEstimator +from gfn.utils.common import set_seed DEFAULT_SEED = 4444 @@ -86,7 +86,7 @@ def estimate_jsd(kde1, kde2): def main(args): # noqa: C901 seed = args.seed if args.seed != 0 else DEFAULT_SEED - torch.manual_seed(seed) + set_seed(seed) device_str = "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" @@ -157,14 +157,14 @@ def main(args): # noqa: C901 pf=pf_estimator, pb=pb_estimator, logF=logF_estimator, - on_policy=True, + off_policy=False, ) else: gflownet = SubTBGFlowNet( pf=pf_estimator, pb=pb_estimator, logF=logF_estimator, - on_policy=True, + off_policy=False, weighting=args.subTB_weighting, lamda=args.subTB_lambda, ) @@ -172,13 +172,13 @@ def main(args): # noqa: C901 gflownet = TBGFlowNet( pf=pf_estimator, pb=pb_estimator, - on_policy=True, + off_policy=False, ) elif args.loss == "ZVar": gflownet = LogPartitionVarianceGFlowNet( pf=pf_estimator, pb=pb_estimator, - on_policy=True, + off_policy=False, ) assert gflownet is not None, f"No gflownet for loss {args.loss}" @@ -231,7 +231,9 @@ def main(args): # noqa: C901 if iteration % 1000 == 0: print(f"current optimizer LR: {optimizer.param_groups[0]['lr']}") - trajectories = gflownet.sample_trajectories(env, n_samples=args.batch_size) + trajectories = gflownet.sample_trajectories( + env, sample_off_policy=False, n_samples=args.batch_size + ) training_samples = gflownet.to_training_samples(trajectories) diff --git a/tutorials/examples/train_discreteebm.py b/tutorials/examples/train_discreteebm.py index f5e35a98..33aa1cc8 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 @@ -20,15 +19,16 @@ from gfn.gflownet import FMGFlowNet from gfn.gym import DiscreteEBM from gfn.modules import DiscretePolicyEstimator -from gfn.utils.common import validate +from gfn.utils.common import set_seed from gfn.utils.modules import NeuralNet, Tabular +from gfn.utils.training import validate DEFAULT_SEED = 4444 def main(args): # noqa: C901 seed = args.seed if args.seed != 0 else DEFAULT_SEED - torch.manual_seed(seed) + set_seed(seed) device_str = "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" @@ -69,7 +69,9 @@ def main(args): # noqa: C901 n_iterations = args.n_trajectories // args.batch_size validation_info = {"l1_dist": float("inf")} for iteration in trange(n_iterations): - trajectories = gflownet.sample_trajectories(env, n_samples=args.batch_size) + trajectories = gflownet.sample_trajectories( + env, off_policy=False, n_samples=args.batch_size + ) training_samples = gflownet.to_training_samples(trajectories) optimizer.zero_grad() diff --git a/tutorials/examples/train_hypergrid.py b/tutorials/examples/train_hypergrid.py index 368d9243..e3301cdd 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 @@ -28,16 +27,17 @@ ) from gfn.gym import HyperGrid from gfn.modules import DiscretePolicyEstimator, ScalarEstimator -from gfn.utils.common import validate +from gfn.utils.common import set_seed from gfn.utils.modules import DiscreteUniform, NeuralNet, Tabular +from gfn.utils.training import validate DEFAULT_SEED = 4444 def main(args): # noqa: C901 seed = args.seed if args.seed != 0 else DEFAULT_SEED - torch.manual_seed(seed) - + set_seed(seed) + off_policy_sampling = False if args.replay_buffer_size == 0 else True device_str = "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" use_wandb = len(args.wandb_project) > 0 @@ -122,7 +122,7 @@ def main(args): # noqa: C901 gflownet = ModifiedDBGFlowNet( pf_estimator, pb_estimator, - True if args.replay_buffer_size == 0 else False, + off_policy_sampling, ) if args.loss in ("DB", "SubTB"): @@ -153,14 +153,14 @@ def main(args): # noqa: C901 pf=pf_estimator, pb=pb_estimator, logF=logF_estimator, - on_policy=True if args.replay_buffer_size == 0 else False, + off_policy=off_policy_sampling, ) else: gflownet = SubTBGFlowNet( pf=pf_estimator, pb=pb_estimator, logF=logF_estimator, - on_policy=True if args.replay_buffer_size == 0 else False, + off_policy=off_policy_sampling, weighting=args.subTB_weighting, lamda=args.subTB_lambda, ) @@ -168,19 +168,18 @@ def main(args): # noqa: C901 gflownet = TBGFlowNet( pf=pf_estimator, pb=pb_estimator, - on_policy=True if args.replay_buffer_size == 0 else False, + off_policy=off_policy_sampling, ) elif args.loss == "ZVar": gflownet = LogPartitionVarianceGFlowNet( pf=pf_estimator, pb=pb_estimator, - on_policy=True if args.replay_buffer_size == 0 else False, + off_policy=off_policy_sampling, ) assert gflownet is not None, f"No gflownet for loss {args.loss}" # Initialize the replay buffer ? - replay_buffer = None if args.replay_buffer_size > 0: if args.loss in ("TB", "SubTB", "ZVar"): @@ -224,7 +223,9 @@ def main(args): # noqa: C901 n_iterations = args.n_trajectories // args.batch_size validation_info = {"l1_dist": float("inf")} for iteration in trange(n_iterations): - trajectories = gflownet.sample_trajectories(env, n_samples=args.batch_size) + trajectories = gflownet.sample_trajectories( + env, n_samples=args.batch_size, sample_off_policy=off_policy_sampling + ) training_samples = gflownet.to_training_samples(trajectories) if replay_buffer is not None: with torch.no_grad(): @@ -290,7 +291,7 @@ def main(args): # noqa: C901 parser.add_argument( "--replay_buffer_size", type=int, - default=0, + default=100, help="If zero, no replay buffer is used. Otherwise, the replay buffer is used.", ) diff --git a/tutorials/examples/train_line.py b/tutorials/examples/train_line.py new file mode 100644 index 00000000..645a6f06 --- /dev/null +++ b/tutorials/examples/train_line.py @@ -0,0 +1,406 @@ +from typing import ClassVar, Literal, Tuple + +import matplotlib.pyplot as plt +import numpy as np +import torch +from torch.distributions import Distribution, Normal # TODO: extend to Beta +from torch.distributions.independent import Independent +from torchtyping import TensorType as TT +from tqdm import trange + +from gfn.actions import Actions +from gfn.env import Env +from gfn.gflownet import TBGFlowNet # TODO: Extend to SubTBGFlowNet +from gfn.modules import GFNModule +from gfn.states import States +from gfn.utils import NeuralNet +from gfn.utils.common import set_seed + + +class Line(Env): + """Mixture of Gaussians Line environment.""" + + def __init__( + self, + mus: list, + sigmas: list, + init_value: float, + n_sd: float = 4.5, + n_steps_per_trajectory: int = 5, + device_str: Literal["cpu", "cuda"] = "cpu", + ): + assert len(mus) == len(sigmas) + self.mus = torch.tensor(mus) + self.sigmas = torch.tensor(sigmas) + self.n_sd = n_sd + self.n_steps_per_trajectory = n_steps_per_trajectory + self.mixture = [Normal(m, s) for m, s in zip(self.mus, self.sigmas)] + + self.init_value = init_value # Used in s0. + self.lb = min(self.mus) - self.n_sd * max(self.sigmas) # Convienience only. + self.ub = max(self.mus) + self.n_sd * max(self.sigmas) # Convienience only. + assert self.lb < self.init_value < self.ub + + s0 = torch.tensor([self.init_value, 0.0], device=torch.device(device_str)) + super().__init__(s0=s0) # sf is -inf. + + def make_States_class(self) -> type[States]: + env = self + + class LineStates(States): + state_shape: ClassVar[Tuple[int, ...]] = (2,) + s0 = env.s0 # should be [init x value, 0]. + sf = env.sf # should be [-inf, -inf]. + + return LineStates + + def make_Actions_class(self) -> type[Actions]: + env = self + + class LineActions(Actions): + action_shape: ClassVar[Tuple[int, ...]] = (1,) # Does not include counter! + dummy_action: ClassVar[TT[2]] = torch.tensor( + [float("inf")], device=env.device + ) + exit_action: ClassVar[TT[2]] = torch.tensor( + [-float("inf")], device=env.device + ) + + return LineActions + + def maskless_step( + self, states: States, actions: Actions + ) -> TT["batch_shape", 2, torch.float]: + states.tensor[..., 0] = states.tensor[..., 0] + actions.tensor.squeeze( + -1 + ) # x position. + states.tensor[..., 1] = states.tensor[..., 1] + 1 # Step counter. + return states.tensor + + def maskless_backward_step( + self, states: States, actions: Actions + ) -> TT["batch_shape", 2, torch.float]: + states.tensor[..., 0] = states.tensor[..., 0] - actions.tensor.squeeze( + -1 + ) # x position. + states.tensor[..., 1] = states.tensor[..., 1] - 1 # Step counter. + return states.tensor + + def is_action_valid( + self, states: States, actions: Actions, backward: bool = False + ) -> bool: + # Can't take a backward step at the beginning of a trajectory. + if torch.any(states[~actions.is_exit].is_initial_state) and backward: + return False + + return True + + def log_reward(self, final_states: States) -> TT["batch_shape", torch.float]: + s = final_states.tensor[..., 0] + # return torch.logsumexp(torch.stack([m.log_prob(s) for m in self.mixture], 0), 0) + + # if s.nelement() == 0: + # return torch.zeros(final_states.batch_shape) + + log_rewards = torch.empty((len(self.mixture),) + final_states.batch_shape) + for i, m in enumerate(self.mixture): + log_rewards[i] = m.log_prob(s) + + return torch.logsumexp(log_rewards, 0) + + @property + def log_partition(self) -> float: + """Log Partition log of the number of gaussians.""" + return torch.tensor(len(self.mus)).log() + + +def render(env, validation_samples=None): + """Renders the reward distribution over the 1D env.""" + x = np.linspace( + min(env.mus) - env.n_sd * max(env.sigmas), + max(env.mus) + env.n_sd * max(env.sigmas), + 100, + ) + + # Get the rewards from our environment. + r = env.States( + torch.tensor(np.stack((x, torch.ones(len(x))), 1)) # Add dummy state counter. + ) + d = torch.exp(env.log_reward(r)) # Plots the reward, not the log reward. + + _, ax1 = plt.subplots() + + if not isinstance(validation_samples, type(None)): + ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis. + ax2.hist( + validation_samples.tensor[:, 0].cpu().numpy(), + bins=100, + density=False, + alpha=0.5, + color="red", + ) + ax2.set_ylabel("Samples", color="red") + ax2.tick_params(axis="y", labelcolor="red") + + ax1.plot(x, d, color="black") + + # Adds the modes. + for mu in env.mus: + ax1.axvline(mu, color="grey", linestyle="--") + + # S0 + ax1.plot([env.init_value], [0], "ro") + ax1.text(env.init_value + 0.1, 0.01, "$S_0$", rotation=45) + + # Means + for i, mu in enumerate(env.mus): + idx = abs(x - mu.numpy()) == min(abs(x - mu.numpy())) + ax1.plot([x[idx]], [d[idx]], "bo") + ax1.text(x[idx] + 0.1, d[idx], "Mode {}".format(i + 1), rotation=0) + + ax1.spines[["right", "top"]].set_visible(False) + ax1.set_ylabel("Reward Value") + ax1.set_xlabel("X Position") + ax1.set_title("Line Environment") + ax1.set_ylim(0, 1) + plt.show() + + +class ScaledGaussianWithOptionalExit(Distribution): + """Extends the Beta distribution by considering the step counter. When sampling, + the step counter can be used to ensure the `exit_action` [inf, inf] is sampled. + """ + + def __init__( + self, + states: TT["n_states", 2], # Tensor of [x position, step counter]. + mus: TT["n_states", 1], # Parameter of Gaussian distribution. + scales: TT["n_states", 1], # Parameter of Gaussian distribution. + backward: bool, + n_steps: int = 5, + ): + # Used to keep track of the "exit" indices for forward/backward trajectories. + self.idx_at_final_forward_step = states[..., 1].tensor == n_steps + self.idx_at_final_backward_step = states[..., 1].tensor == 1 + self.dist = Independent(Normal(mus, scales), len(states.batch_shape)) + self.exit_action = torch.FloatTensor([-float("inf")]).to(states.device) + self.backward = backward + + def sample(self, sample_shape=()): + actions = self.dist.sample(sample_shape) + + # For any state which is at the terminal step, assign the exit action. + if not self.backward: + exit_mask = torch.where(self.idx_at_final_forward_step, 1, 0).bool() + actions[exit_mask] = self.exit_action + + return actions + + def log_prob(self, sampled_actions): + """TODO""" + # The default value of logprobs is 0, because these represent the p=1 event + # of either the terminal forward (Sn->Sf) or backward (S1->S0) transition. + # We do not explicitly fill these values, but rather set the appropriate + # logprobs using the `exit_idx` mask. + logprobs = torch.full_like(sampled_actions, fill_value=0.0) + actions_to_eval = torch.full_like(sampled_actions, 0) # Used to remove infs. + + # TODO: Continous Timestamp Environmemt Subclass. + if self.backward: # Backward: handle the s1->s0 action (always p=1). + exit_idx = self.idx_at_final_backward_step + else: # Forward: handle exit actions: sn->sf. + exit_idx = torch.all(sampled_actions == -float("inf"), 1) + + actions_to_eval[~exit_idx] = sampled_actions[~exit_idx] + if sum(~exit_idx) > 0: + logprobs[~exit_idx] = self.dist.log_prob(actions_to_eval)[ + ~exit_idx + ].unsqueeze(-1) + + return logprobs.squeeze(-1) + + +class GaussianStepNeuralNet(NeuralNet): + """A deep neural network for the forward and backward policy.""" + + def __init__( + self, + hidden_dim: int, + n_hidden_layers: int, + policy_std_min: float = 0.1, + policy_std_max: float = 1, + ): + """Instantiates the neural network for the forward policy.""" + assert policy_std_min > 0 + assert policy_std_min < policy_std_max + self.policy_std_min = policy_std_min + self.policy_std_max = policy_std_max + self.input_dim = 2 # [x_pos, counter]. + self.output_dim = 2 # [mus, scales]. + + super().__init__( + input_dim=self.input_dim, + hidden_dim=hidden_dim, + n_hidden_layers=n_hidden_layers, + output_dim=self.output_dim, + activation_fn="elu", + ) + + def forward( + self, preprocessed_states: TT["batch_shape", 2, float] + ) -> TT["batch_shape", "3"]: + """Calculate the gaussian parameters, applying the bound to sigma.""" + assert preprocessed_states.ndim == 2 + out = super().forward(preprocessed_states) # [..., 2]: represents mean & std. + minmax_norm = self.policy_std_max - self.policy_std_min + out[..., 1] = ( + torch.sigmoid(out[..., 1]) * minmax_norm + self.policy_std_min + ) # Scales / Variances. + + return out + + +class StepEstimator(GFNModule): + """Estimator for PF and PB of the Line environment.""" + + def __init__(self, env: Line, module: torch.nn.Module, backward: bool): + super().__init__(module, is_backward=backward) + self.backward = backward + self.n_steps_per_trajectory = env.n_steps_per_trajectory + + def expected_output_dim(self) -> int: + return 2 # [locs, scales]. + + def to_probability_distribution( + self, + states: States, + module_output: TT["batch_shape", "output_dim", float], + scale_factor=0, # policy_kwarg. + ) -> Distribution: + assert len(states.batch_shape) == 1 + assert module_output.shape == states.batch_shape + (2,) # [locs, scales]. + locs, scales = torch.split(module_output, [1, 1], dim=-1) + + return ScaledGaussianWithOptionalExit( + states, + locs, + scales + scale_factor, # Increase this value to induce exploration. + backward=self.backward, + n_steps=self.n_steps_per_trajectory, + ) + + +def train( + gflownet, + env, + seed=4444, + n_trajectories=3e6, + batch_size=128, + lr_base=1e-3, + gradient_clip_value=5, + exploration_var_starting_val=2, +): + """Trains a GFlowNet on the Line Environment.""" + set_seed(seed) + n_iterations = int(n_trajectories // batch_size) + + # TODO: Add in the uniform pb demo? + # uniform_pb = False + # + # if uniform_pb: + # pb_module = BoxPBUniform() + # else: + # pb_module = BoxPBNeuralNet(hidden_dim, n_hidden_layers, n_components) + + # 3. Create the optimizer and scheduler. + optimizer = torch.optim.Adam(gflownet.pf_pb_parameters(), lr=lr_base) + lr_logZ = lr_base * 100 + optimizer.add_param_group({"params": gflownet.logz_parameters(), "lr": lr_logZ}) + + # Training loop. + states_visited = 0 + tbar = trange(n_iterations, desc="Training iter") + scale_schedule = np.linspace(exploration_var_starting_val, 0, n_iterations) + + for iteration in tbar: + optimizer.zero_grad() + # Off Policy Sampling. + trajectories = gflownet.sample_trajectories( + env, + n_samples=batch_size, + sample_off_policy=True, + scale_factor=scale_schedule[iteration], # Off policy kwargs. + ) + training_samples = gflownet.to_training_samples(trajectories) + loss = gflownet.loss(env, training_samples) + loss.backward() + + # Gradient Clipping. + for p in gflownet.parameters(): + if p.ndim > 0 and p.grad is not None: # We do not clip logZ grad. + p.grad.data.clamp_( + -gradient_clip_value, gradient_clip_value + ).nan_to_num_(0.0) + + optimizer.step() + states_visited += len(trajectories) + + tbar.set_description( + "Training iter {}: (states visited={}, loss={:.3f}, estimated logZ={:.3f}, true logZ={:.3f})".format( + iteration, + states_visited, + loss.item(), + gflownet.logz_parameters()[ + 0 + ].item(), # Assumes only one estimate of logZ. + env.log_partition, + ) + ) + + return gflownet + + +if __name__ == "__main__": + environment = Line( + mus=[2, 5], + sigmas=[0.5, 0.5], + init_value=0, + n_sd=4.5, + n_steps_per_trajectory=5, + ) + + # Hyperparameters. + hid_dim = 64 + n_hidden_layers = 2 + policy_std_min = 0.1 # Lower bound of sigma that can be predicted by policy. + policy_std_max = 1 # Upper bound of sigma that can be predicted by policy. + exploration_var_starting_val = 2 # Used for off-policy training. + + pf_module = GaussianStepNeuralNet( + hidden_dim=hid_dim, + n_hidden_layers=n_hidden_layers, + policy_std_min=policy_std_min, + policy_std_max=policy_std_max, + ) + pf = StepEstimator(environment, pf_module, backward=False) + + pb_module = GaussianStepNeuralNet( + hidden_dim=hid_dim, + n_hidden_layers=n_hidden_layers, + policy_std_min=policy_std_min, + policy_std_max=policy_std_max, + ) + pb = StepEstimator(environment, pb_module, backward=True) + gflownet = TBGFlowNet(pf=pf, pb=pb, off_policy=False, init_logZ=0.0) + + gflownet = train( + gflownet, + environment, + lr_base=1e-3, + n_trajectories=1.28e6, + batch_size=256, + exploration_var_starting_val=exploration_var_starting_val, + ) + + validation_samples = gflownet.sample_terminating_states(environment, 10000) + render(environment, validation_samples=validation_samples) diff --git a/tutorials/notebooks/intro_gfn_continuous_line.ipynb b/tutorials/notebooks/intro_gfn_continuous_line.ipynb new file mode 100644 index 00000000..086f5c33 --- /dev/null +++ b/tutorials/notebooks/intro_gfn_continuous_line.ipynb @@ -0,0 +1,865 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "EVRi3TLFMqK8" + }, + "outputs": [], + "source": [ + "from tqdm import tqdm, trange\n", + "from typing import ClassVar, Literal, Tuple # ClassVar, Literal, Tuple, cast\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import random\n", + "import scipy.stats as stats\n", + "\n", + "from torchtyping import TensorType as TT\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.distributions import Distribution, Normal, Beta\n", + "\n", + "from gfn.actions import Actions\n", + "from gfn.env import Env\n", + "from gfn.gflownet import TBGFlowNet, SubTBGFlowNet\n", + "from gfn.modules import GFNModule\n", + "from gfn.states import States\n", + "from gfn.utils import NeuralNet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JzaTmAlid5G9" + }, + "source": [ + "Here, we are explore Continuous GFlowNets in an exceedingly simple case: from an initial starting point on the number line, sample a set of positivei increments such that we learn to sample from some reward distribution. Here, that reward distribution will be some mixture of Gaussians. Each step will be sampled from a scaled Beta distribution.\n", + "\n", + "The key difference with Continuous GFlowNets is that they sample some *delta* in a continuous space, instead of discrete actions. Typically, this means your GFlowNet uses a function approximator $f(\\cdot)$, which accepts the current state $s_{t}$, to predict the *paramaters of a distribution* $\\rho = \\{p_1, p_2, ..., p_n\\}$. Then your choses distribution $D(\\rho)$ is used to sample a real-valued tensor $s_{\\Delta} \\sim D(\\rho)$ which is added to your current state to produce a the next step in the state space $s_{t+1} = s_{t} + s_{\\Delta}$ (note, we no longer consider a DAG here, but rather a topological space with distinguished source and sink states).\n", + "\n", + "In our case, we want to increment along the number line in such a way that we learn to sample from some arbitrary multi-modal distribution. So we need a distribution from which to sample these steps. Recall that the probability density function of the beta distribution, for $0 \\leq x \\leq 1$ or $0 < x < 1$, given two shape parameters $\\alpha, \\beta > 0$, is\n", + "\n", + "$$f(x;\\alpha,\\beta) = constant\\ \\cdot\\ x^{\\alpha-1}(1-x)^{\\beta-1} = \\frac{1}{B(\\alpha,\\beta)} x^{\\alpha-1}(1-x)^{\\beta-1}$$\n", + "\n", + ", where $B(\\alpha,\\beta) = \\frac{\\Gamma(\\alpha)\\Gamma(\\beta)}{\\Gamma(\\alpha + \\beta)}$, and $\\Gamma(z)$ is the Gamma function, see [here](https://en.wikipedia.org/wiki/Beta_distribution).\n", + "\n", + "This distribution has a nice property for our setup: the distribution is bounded and can be made strictly positive, so each \"step\" sampled will increment the number line. To take steps larger than one, we can simply also predict a scaling factor $\\gamma$ to apply to our beta distribution samples.\n", + "\n", + "In our setup, we will define a multimodal distribution on the 1D line. We will also define a starting point $S_0$, on the number line to the left of all modes by an arbitrary distance. The GFlowNet must sample increments along the number line such that it samples final values along the number line proportionally to the mixture distribution.\n", + "\n", + "We also need to know when to terminate this process, otherwise we never sample a final value. To do so, let's simply include the count value, $t$, in the state $s_t$, and always terminate when $t=5$. There are more sophisitcated ways to do this, but they add complexity, and we want to focus this tutorial on only the core concepts.\n", + "\n", + "Since every state reachable by the backward policy must also be reachable by the forward policy, we need to enforce that the smallest value that can be sampled by the backward policy is $S_0$. The forward policy will naturally satisfy this constraint.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6wr4-GXH6uLd" + }, + "source": [ + "# Defining the Environment\n", + "\n", + "First let's define our environment. We require a few things. First, we need a reward distribution. This will be a mixture of Gaussians on the real number line. The reward at each point will simply be the sum of the PDFs at that point across all elements of the mixture. To keep things simple, we'll enforce that all trajectories are exactly 5 steps. With probabilisitc exit actions, the logic becomes more tricky, though it is often useful in some applications. Finally, we need to define $S_0$, the starting point for all trajectories. This `lower_bound` (aka `lb`) will be defined as a configurable number of standard deviations below the gaussian with the smallest mean value.\n", + "\n", + "For each forward action, we will add the action value to the current state, and increment the step counter. For the backward action, we will substract the action value from the current state, and decrement the step counter.\n", + "\n", + "We're not allowed to go backward beyond $S_0$, and this constraint is handled by the distribution we sample from for the backward policy, detailed later." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "V88hPnJ568av", + "outputId": "f9caff7d-931f-49f9-a145-d9e855717ce5" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_145438/1097605799.py:20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " Normal(torch.tensor(m), torch.tensor(s)) for m, s in zip(mus, self.sigmas)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class Line(Env):\n", + " \"\"\"Line environment.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " mus: list = [-1, 1],\n", + " variances: list = [0.2, 0.2],\n", + " n_sd: float = 4.5,\n", + " init_value: float = 0,\n", + " n_steps_per_trajectory: int = 5,\n", + " device_str: Literal[\"cpu\", \"cuda\"] = \"cpu\",\n", + " ):\n", + " assert len(mus) == len(variances)\n", + " self.mus = torch.tensor(mus)\n", + " self.sigmas = torch.tensor([math.sqrt(v) for v in variances])\n", + " self.variances = torch.tensor(variances)\n", + " self.n_sd = n_sd\n", + " self.n_steps_per_trajectory = n_steps_per_trajectory\n", + " self.mixture = [\n", + " Normal(torch.tensor(m), torch.tensor(s)) for m, s in zip(mus, self.sigmas)\n", + " ]\n", + "\n", + " self.init_value = init_value # Used in s0.\n", + " self.lb = min(self.mus) - self.n_sd * max(self.sigmas) # Convienience only.\n", + " self.ub = max(self.mus) + self.n_sd * max(self.sigmas) # Convienience only.\n", + "\n", + " assert self.lb < self.init_value < self.ub\n", + "\n", + " # The state is [x_value, count]. x_value is initalized close to the lower bound.\n", + " s0 = torch.tensor([self.init_value, 0.0], device=torch.device(device_str))\n", + " sf = torch.FloatTensor([float(\"inf\"), float(\"inf\")], ).to(s0.device)\n", + " super().__init__(s0=s0, sf=sf) # Overwriting the default sf of -inf.\n", + "\n", + " def make_States_class(self) -> type[States]:\n", + " env = self\n", + "\n", + " class LineStates(States):\n", + " state_shape: ClassVar[Tuple[int, ...]] = (2,)\n", + " s0 = env.s0 # should be [init value, 0].\n", + " sf = env.sf # should be [+inf, +inf].\n", + "\n", + " @classmethod\n", + " def make_random_states_tensor(cls, batch_shape: Tuple[int, ...]) -> TT[\"batch_shape\", 2, torch.float]:\n", + " # Scale [0, 1] values between lower & upper bound.\n", + " scaling = (self.ub - self.lb) + self.lb\n", + " x_val = torch.rand(batch_shape + (1,)) * scaling\n", + " steps = torch.full(batch_shape + (1,), self.n_steps_per_trajectory)\n", + " return torch.cat((x_val, steps), dim=-1, device=env.device)\n", + "\n", + " return LineStates\n", + "\n", + " def make_Actions_class(self) -> type[Actions]:\n", + " env = self\n", + "\n", + " class LineActions(Actions):\n", + " action_shape: ClassVar[Tuple[int, ...]] = (1,) # Does not include counter!\n", + " dummy_action: ClassVar[TT[2]] = torch.tensor([-float(\"inf\")], device=env.device)\n", + " exit_action: ClassVar[TT[2]] = torch.tensor([float(\"inf\")], device=env.device)\n", + "\n", + " return LineActions\n", + "\n", + " def maskless_step(self, states: States, actions: Actions) -> TT[\"batch_shape\", 2, torch.float]:\n", + " states.tensor[..., 0] = states.tensor[..., 0] + actions.tensor.squeeze(-1) # x position.\n", + " states.tensor[..., 1] = states.tensor[..., 1] + 1 # Step counter.\n", + " return states.tensor\n", + "\n", + " def maskless_backward_step(self, states: States, actions: Actions) -> TT[\"batch_shape\", 2, torch.float]:\n", + " states.tensor[..., 0] = states.tensor[..., 0] - actions.tensor.squeeze(-1) # x position.\n", + " states.tensor[..., 1] = states.tensor[..., 1] - 1 # Step counter.\n", + " return states.tensor\n", + "\n", + " def is_action_valid(self, states: States, actions: Actions, backward: bool = False) -> bool:\n", + " \"\"\"We are only going to prevent taking actions leftward beyond `S_0`.\"\"\"\n", + " non_exit_actions = actions[~actions.is_exit]\n", + " non_terminal_states = states[~actions.is_exit]\n", + " s0_states_idx = non_terminal_states.is_initial_state\n", + "\n", + " # Can't take a backward step at the beginning of a trajectory.\n", + " if torch.any(s0_states_idx) and backward:\n", + " return False\n", + "\n", + " non_s0_states = non_terminal_states[~s0_states_idx].tensor\n", + " non_s0_actions = non_exit_actions[~s0_states_idx].tensor\n", + "\n", + " return True\n", + "\n", + " def reward(self, final_states: States) -> TT[\"batch_shape\", torch.float]:\n", + " \"\"\"Sum of the exponential of each log probability in the mixture.\"\"\"\n", + " r = torch.zeros(final_states.batch_shape)\n", + " for m in self.mixture:\n", + " r = r + torch.exp(m.log_prob(final_states.tensor[..., 0])) # x position.\n", + "\n", + " return r\n", + "\n", + " def log_reward(self, final_states: States) -> TT[\"batch_shape\", torch.float]:\n", + " return torch.log(self.reward(final_states))\n", + "\n", + " @property\n", + " def log_partition(self) -> float:\n", + " \"\"\"Log Partition log of the number of gaussians.\"\"\"\n", + " return torch.tensor(len(self.mus)).log()\n", + "\n", + "\n", + "def render(env, validation_samples=None):\n", + " \"\"\"Renders the reward distribution over the 1D env.\"\"\"\n", + " x = np.linspace(\n", + " min(env.mus) - env.n_sd * max(env.sigmas),\n", + " max(env.mus) + env.n_sd * max(env.sigmas),\n", + " 100,\n", + " )\n", + "\n", + " d = np.zeros(x.shape)\n", + " for mu, sigma in zip(env.mus, env.sigmas):\n", + " d += stats.norm.pdf(x, mu, sigma)\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " if not isinstance(validation_samples, type(None)):\n", + " ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis.\n", + " ax2.hist(\n", + " validation_samples.tensor[:,0].cpu().numpy(),\n", + " bins=100,\n", + " density=False,\n", + " alpha=0.5,\n", + " color=\"red\",\n", + " )\n", + " ax2.set_ylabel(\"Samples\", color=\"red\")\n", + " ax2.tick_params(axis=\"y\", labelcolor=\"red\")\n", + "\n", + " ax1.plot(x, d, color=\"black\")\n", + "\n", + " # Adds the modes.\n", + " for mu in env.mus:\n", + " ax1.axvline(mu, color=\"grey\", linestyle=\"--\")\n", + "\n", + " # S0\n", + " ax1.plot([env.init_value], [0], 'ro')\n", + " ax1.text(env.init_value + 0.1, 0.01, \"$S_0$\", rotation=45)\n", + "\n", + " # Means\n", + " for i, mu in enumerate(env.mus):\n", + " idx = abs(x - mu.numpy()) == min(abs(x - mu.numpy()))\n", + " ax1.plot([x[idx]], [d[idx]], 'bo')\n", + " ax1.text(x[idx] + 0.1, d[idx], \"Mode {}\".format(i + 1), rotation=0)\n", + "\n", + " ax1.spines[['right', 'top']].set_visible(False)\n", + " ax1.set_ylabel(\"Reward Value\")\n", + " ax1.set_xlabel(\"X Position\")\n", + " ax1.set_title(\"Line Environment\")\n", + " ax1.set_ylim(0, 1)\n", + " plt.show()\n", + "\n", + "# Set up our simple environment.\n", + "env = Line(mus=[-1, 1], variances=[0.2, 0.2], n_sd=4.5, init_value=0, n_steps_per_trajectory=5)\n", + "render(env)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Uav05LS-6ygH" + }, + "source": [ + "# Defining Action Distributions\n", + "\n", + "We're going to use Trajectory Balance, so we first need to define our forward and backward policy. The first step is to define the distribution we will use for sampling forward and backward actions. Luckily, the Beta distribution itself is already defined. We just need to make a slight extension of it which handles exits (for the forward policy) and the scaling factor:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 246 + }, + "id": "Z85mN47piGB3", + "outputId": "20ddf817-5f8b-4455-ab87-6eff45f35cf1" + }, + "outputs": [], + "source": [ + "class ScaledGaussianWithOptionalExit(Distribution):\n", + " \"\"\"Extends the Beta distribution by considering the step counter. When sampling,\n", + " the step counter can be used to ensure the `exit_action` [inf, inf] is sampled.\n", + " \"\"\"\n", + " def __init__(\n", + " self,\n", + " states: TT[\"n_states\", 2], # Tensor of [x position, step counter].\n", + " mus: TT[\"n_states\", 1], # Parameter of Gaussian distribution.\n", + " scales: TT[\"n_states\", 1], # Parameter of Gaussian distribution.\n", + " forward: bool, # True for forward policies.\n", + " n_steps: int = 5,\n", + " ):\n", + " self.states = states\n", + " self.n_steps = n_steps\n", + " self.dist = Normal(mus, scales)\n", + " self.exit_action = torch.FloatTensor([float(\"inf\")]).to(states.device)\n", + " self.forward = forward\n", + "\n", + " def sample(self, sample_shape=()):\n", + " actions = self.dist.sample(sample_shape)\n", + "\n", + " # For any state which is at the terminal step, assign the exit action.\n", + " if self.allow_exit:\n", + " exit_mask = torch.where(\n", + " self.states[..., -1].tensor >= self.n_steps, # This is the step counter.\n", + " torch.ones(sample_shape + (1,)),\n", + " torch.zeros(sample_shape + (1,)),\n", + " ).bool()\n", + " actions[exit_mask] = self.exit_action\n", + "\n", + " return actions\n", + "\n", + " def log_prob(self, sampled_actions):\n", + " \"\"\"TODO\"\"\"\n", + " # These are the exited states.\n", + " exit = torch.all(sampled_actions == torch.full_like(sampled_actions[0], float(\"inf\")), 1) # This is the exit action\n", + " logprobs = torch.full_like(sampled_actions, fill_value=-float(\"inf\"))\n", + "\n", + " if self.forward: # Forward: handle exit actions.\n", + " if sum(~exit) > 0:\n", + " # print(\"samples_actions_shape={}, logprobs_shape={}, exit_shape={}\".format(\n", + " # sampled_actions.shape, logprobs.shape, exit.shape))\n", + " logprobs[~exit] = self.dist.log_prob(sampled_actions)[~exit]\n", + " logprobs[exit] = torch.log(torch.tensor(1.0)) # p(exit) == 1 at n_steps, else 0.\n", + " else: # Backward: handle the transition from S1 -> S0.\n", + " logprobs = self.dist.log_prob(sampled_actions)\n", + "\n", + " return logprobs.squeeze(-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mgdNQgNvpGaf" + }, + "source": [ + "Next, we need a neural network which will predict, at each step, the parameters of this distribution, for both the forward and backward policies. Note the logic which enforces that no backward step can go beyond the $S_0$ value." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "60eBIg57oeCw" + }, + "outputs": [], + "source": [ + "class GaussianStepNeuralNet(NeuralNet):\n", + " \"\"\"A deep neural network for the forward and backward policy.\"\"\"\n", + " def __init__(\n", + " self,\n", + " hidden_dim: int,\n", + " n_hidden_layers: int,\n", + " backward: bool,\n", + " s0_val: float = -float(\"inf\"),\n", + " policy_std_min: float = 0.1,\n", + " policy_std_max: float = 1,\n", + " ):\n", + " \"\"\"Instantiates the neural network for the forward policy.\"\"\"\n", + " assert policy_std_min > 0\n", + " assert policy_std_min < policy_std_max\n", + "\n", + " self.input_dim = 2 # [x_pos, counter]\n", + " self.output_dim = 2 # [mus, scales]\n", + " self.s0_val = s0_val\n", + " self.backward = backward\n", + " self.policy_std_min = policy_std_min\n", + " self.policy_std_max = policy_std_max\n", + "\n", + " if backward:\n", + " assert not math.isinf(s0_val)\n", + "\n", + " super().__init__(\n", + " input_dim=self.input_dim,\n", + " hidden_dim=hidden_dim,\n", + " n_hidden_layers=n_hidden_layers,\n", + " output_dim=self.output_dim,\n", + " activation_fn=\"elu\",\n", + " )\n", + "\n", + " def forward(self, preprocessed_states: TT[\"batch_shape\", 2, float]) -> TT[\"batch_shape\", \"3\"]:\n", + " assert preprocessed_states.ndim == 2\n", + " out = super().forward(preprocessed_states) # [..., 2]: represents mean & std.\n", + "\n", + " # When forward, the mean can take any value. The variance must be > 0.1\n", + " minmax_norm = (self.policy_std_max - self.policy_std_min) + self.policy_std_min\n", + " out[..., 1] = torch.sigmoid(out[..., 1]) * minmax_norm # Scales / Variances.\n", + "\n", + " if self.backward:\n", + " distance_to_s0 = preprocessed_states[..., 0] - self.s0_val\n", + "\n", + " # At backward_step = 1, where the next step is s0, the only valid action\n", + " # to to jump directly to s0.\n", + " idx_to_s0 = preprocessed_states[..., 1] == 1 # s_1 -> s_0.\n", + " if sum(idx_to_s0) > 0:\n", + " out[idx_to_s0, 0] = distance_to_s0[idx_to_s0]\n", + " out[idx_to_s0, 1] = 1/(2*np.pi)**0.5 # Gaussian PDF scaling factor.\n", + "\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZIfAHKhryu1i" + }, + "source": [ + "Next, we need an `Estimator` that holds our function approximator. This accepts states, and returns a distribution we can sample from:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "mohEPld6yvJz" + }, + "outputs": [], + "source": [ + "class StepEstimator(GFNModule):\n", + " \"\"\"Estimator for P_F and P_B of the Line environment.\"\"\"\n", + " def __init__(self, env: Line, module: torch.nn.Module, forward: bool):\n", + " super().__init__(module)\n", + " self.forward = forward\n", + "\n", + " def expected_output_dim(self) -> int:\n", + " return 2 # [locs, scales].\n", + "\n", + " def to_probability_distribution(\n", + " self,\n", + " states: States,\n", + " module_output: TT[\"batch_shape\", \"output_dim\", float],\n", + " scale_factor = 1,\n", + " ) -> Distribution:\n", + " # First, we verify that the batch shape of states is 1\n", + " assert len(states.batch_shape) == 1\n", + " assert module_output.shape == states.batch_shape + (2,) # [locs, scales].\n", + " locs, scales = torch.split(module_output, [1, 1], dim=-1)\n", + "\n", + " return ScaledGaussianWithOptionalExit(\n", + " states,\n", + " locs,\n", + " scales + scale_factor, # Increase this value to induce exploration.\n", + " allow_exit=self.forward,\n", + " n_steps=env.n_steps_per_trajectory,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "KWVDCPoVRQXH" + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "StepEstimator.__init__() got an unexpected keyword argument 'allow_exit'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/jdv/code/torchgfn/tutorials/notebooks/intro_gfn_continuous_line.ipynb Cell 11\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 126\u001b[0m hid_dim \u001b[39m=\u001b[39m \u001b[39m32\u001b[39m\n\u001b[1;32m 128\u001b[0m pf_module \u001b[39m=\u001b[39m GaussianStepNeuralNet(hidden_dim\u001b[39m=\u001b[39mhid_dim, n_hidden_layers\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m, backward\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m--> 129\u001b[0m pf_estimator \u001b[39m=\u001b[39m StepEstimator(\n\u001b[1;32m 130\u001b[0m env,\n\u001b[1;32m 131\u001b[0m pf_module,\n\u001b[1;32m 132\u001b[0m allow_exit \u001b[39m=\u001b[39;49m \u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m 133\u001b[0m )\n\u001b[1;32m 135\u001b[0m pb_module \u001b[39m=\u001b[39m GaussianStepNeuralNet(hidden_dim\u001b[39m=\u001b[39mhid_dim, n_hidden_layers\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m, backward\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, s0_val\u001b[39m=\u001b[39menv\u001b[39m.\u001b[39minit_value)\n\u001b[1;32m 136\u001b[0m pb_estimator \u001b[39m=\u001b[39m StepEstimator(\n\u001b[1;32m 137\u001b[0m env,\n\u001b[1;32m 138\u001b[0m pb_module,\n\u001b[1;32m 139\u001b[0m allow_exit \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m 140\u001b[0m )\n", + "\u001b[0;31mTypeError\u001b[0m: StepEstimator.__init__() got an unexpected keyword argument 'allow_exit'" + ] + } + ], + "source": [ + "def get_scheduler(optim, n_iter, n_steps_scheduler=1500, scheduler_gamma=0.5):\n", + " return torch.optim.lr_scheduler.MultiStepLR(\n", + " optim,\n", + " milestones=[\n", + " i * n_steps_scheduler\n", + " for i in range(1, 1 + int(n_iter / n_steps_scheduler))\n", + " ],\n", + " gamma=scheduler_gamma,\n", + " )\n", + "\n", + "def train(seed=4444, n_trajectories=3e6, batch_size=128, lr_base=1e-3, gradient_clip_value=10, n_logz_resets=0):\n", + " # Reproducibility.\n", + " torch.manual_seed(seed)\n", + " random.seed(seed)\n", + " np.random.seed(seed)\n", + " torch.manual_seed(seed)\n", + " torch.backends.cudnn.deterministic = True\n", + " torch.backends.cudnn.benchmark = False\n", + " torch.manual_seed(seed)\n", + "\n", + " device_str = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + " n_iterations = int(n_trajectories // batch_size)\n", + " logz_reset_interval = 50\n", + " logz_reset_count = 0\n", + "\n", + "\n", + " # TODO: Add in the uniform pb demo?\n", + " #uniform_pb = False\n", + " #\n", + " #if uniform_pb:\n", + " # pb_module = BoxPBUniform()\n", + " #else:\n", + " # pb_module = BoxPBNeuralNet(hidden_dim, n_hidden_layers, n_components)\n", + "\n", + " # 3. Create the optimizer and scheduler.\n", + " optimizer = torch.optim.Adam(pf_module.parameters(), lr=lr_base)\n", + " logZ = dict(gflownet.named_parameters())[\"logZ\"]\n", + " optimizer.add_param_group({\"params\": [logZ], \"lr\": lr_base * 10})\n", + "\n", + " # TODO:\n", + " # if not uniform_pb:\n", + " # optimizer.add_param_group({\"params\": pb_module.parameters(), \"lr\": lr_base})\n", + " # optimizer.add_param_group({\"params\": logFmodule.parameters(), \"lr\": lr_logF})\n", + "\n", + " scheduler = get_scheduler(\n", + " optimizer,\n", + " n_iterations,\n", + " n_steps_scheduler=int(n_iterations / 2),\n", + " scheduler_gamma=0.5,\n", + " )\n", + "\n", + " # TODO:\n", + " # 4. Sample from the true reward distribution, and fit a KDE to the samples.\n", + " n_val_samples = 1000\n", + " # samples_from_reward = sample_from_reward(env, n_samples=n_val_samples)\n", + " # true_kde = KernelDensity(kernel=\"exponential\", bandwidth=0.1).fit(\n", + " # samples_from_reward\n", + " # )\n", + "\n", + " # Training loop!\n", + " validation_interval = 1e4\n", + " states_visited = 0\n", + " jsd = float(\"inf\")\n", + " tbar = trange(n_iterations, desc=\"Training iter\")\n", + " scale_schedule = np.linspace(2, 0, n_iterations)\n", + " for iteration in tbar:\n", + "\n", + " if logz_reset_count < n_logz_resets and iteration % logz_reset_interval == 0:\n", + " gflownet.logZ = torch.nn.init.constant_(gflownet.logZ, 0)\n", + " print(\"resetting logz\")\n", + " logz_reset_count += 1\n", + "\n", + " # Off Policy Sampling.\n", + " trajectories = gflownet.sample_trajectories(\n", + " env,\n", + " n_samples=batch_size,\n", + " scale_factor=scale_schedule[iteration],\n", + " )\n", + " training_samples = gflownet.to_training_samples(trajectories)\n", + "\n", + " optimizer.zero_grad()\n", + " loss = gflownet.loss(env, training_samples)\n", + " loss.backward()\n", + "\n", + " # LESSON: Clipping\n", + " for p in gflownet.parameters():\n", + " if p.ndim > 0 and p.grad is not None: # We do not clip logZ grad.\n", + " p.grad.data.clamp_(-gradient_clip_value, gradient_clip_value).nan_to_num_(0.0)\n", + "\n", + " optimizer.step()\n", + " scheduler.step()\n", + "\n", + " states_visited += len(trajectories)\n", + " assert logZ is not None\n", + "\n", + " #to_log = {\"loss\": loss.item(), \"states_visited\": states_visited}\n", + " # logZ_info = \"\"\n", + " #to_log.update({\"logZdiff\": env.log_partition - logZ.item()})\n", + " # logZ_info = f\"logZ: {:.2f}, \"\n", + " #print(logFmodule.logZ_value)\n", + " #print(gflownet.logZ)\n", + "\n", + " tbar.set_description(\"Training iter {}: (states visited={}, loss={:.3f}, estimated logZ={:.3f}, true logZ={:.3f}, JSD={}, LR={})\".format(\n", + " iteration,\n", + " states_visited,\n", + " loss.item(),\n", + " logZ.item(),\n", + " env.log_partition,\n", + " jsd,\n", + " optimizer.param_groups[0]['lr'],\n", + " )\n", + " )\n", + " if iteration % validation_interval == 0:\n", + " validation_samples = gflownet.sample_terminating_states(env, n_val_samples)\n", + " # kde = KernelDensity(kernel=\"exponential\", bandwidth=0.1).fit(\n", + " # validation_samples.tensor.detach().cpu().numpy()\n", + " # )\n", + " # jsd = estimate_jsd(kde, true_kde)\n", + " #to_log.update({\"JSD\": jsd})\n", + "\n", + " return jsd\n", + "\n", + "# Forward and backward policy estimators. We pass the lower bound from the env here.\n", + "hid_dim = 32\n", + "\n", + "pf_module = GaussianStepNeuralNet(hidden_dim=hid_dim, n_hidden_layers=2, backward=False)\n", + "pf_estimator = StepEstimator(\n", + " env,\n", + " pf_module,\n", + " forward = True,\n", + ")\n", + "\n", + "pb_module = GaussianStepNeuralNet(hidden_dim=hid_dim, n_hidden_layers=2, backward=True, s0_val=env.init_value)\n", + "pb_estimator = StepEstimator(\n", + " env,\n", + " pb_module,\n", + " forward = False,\n", + ")\n", + "\n", + "gflownet = TBGFlowNet(\n", + " pf=pf_estimator,\n", + " pb=pb_estimator,\n", + " off_policy=True,\n", + " init_logZ=0.0,\n", + ")\n", + "\n", + "# Magic hyperparameters: lr_base=4e-2, n_trajectories=3e6, batch_size=2048\n", + "train(lr_base=1e-3, n_trajectories=1e6, batch_size=1024) # I started training this with 1e-3 and then reduced it.\n", + "validation_samples = gflownet.sample_terminating_states(env, 10000)\n", + "render(env, validation_samples=validation_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "amO8d6IsDOpN" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "id": "-5HBIeq7DOpO", + "outputId": "1c80da44-c569-4c96-ca23-4988393951e2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "steps per trajectory: 5\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"steps per trajectory: {}\".format(env.n_steps_per_trajectory))\n", + "trajs = gflownet.sample_trajectories(env, n_samples=1000)\n", + "trajs = torch.stack( [ trajs[i].states.tensor.view(-1,2)[:-1,0] for i in range(1000) ])\n", + "for i in range(1000):\n", + " plt.plot(\n", + " np.arange(env.n_steps_per_trajectory + 1),\n", + " trajs[i].cpu().numpy(),\n", + " alpha=0.3,\n", + " linewidth=0.1,\n", + " color='black'\n", + " )\n", + "plt.xlabel('step')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "29xRFWjIKwe-", + "outputId": "48ba577e-6f94-41a3-ae8b-22aa98a85b7f" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_143291/2351962954.py:21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " Normal(torch.tensor(m), torch.tensor(s)) for m, s in zip(mus, self.sigmas)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#env = Line(mus=[-1, 1], variances=[1, 1], n_sd=4.5, init_value=0, n_steps_per_trajectory=5)\n", + "env = Line(mus=[-7, -3], variances=[0.4, 0.2], n_sd=4.5, init_value=-1, n_steps_per_trajectory=5)\n", + "\n", + "render(env)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "uDWoE1X9hS0r", + "outputId": "8f7666ae-fbcc-4dad-835d-be9bfd1506ed" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training iter 487: (states visited=999424, loss=15.941, estimated logZ=-7.198, true logZ=0.693, JSD=inf, LR=0.01): 100%|██████████| 488/488 [02:22<00:00, 3.42it/s] \n" + ] + }, + { + "data": { + "text/plain": [ + "inf" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Forward and backward policy estimators. We pass the lower bound from the env here.\n", + "hid_dim = 32\n", + "\n", + "pf_module = GaussianStepNeuralNet(hidden_dim=hid_dim, n_hidden_layers=2, backward=False)\n", + "pf_estimator = StepEstimator(\n", + " env,\n", + " pf_module,\n", + " allow_exit = True,\n", + ")\n", + "\n", + "pb_module = GaussianStepNeuralNet(hidden_dim=hid_dim, n_hidden_layers=2, backward=True, s0_val=env.init_value)\n", + "pb_estimator = StepEstimator(\n", + " env,\n", + " pb_module,\n", + " allow_exit = False,\n", + ")\n", + "\n", + "gflownet = TBGFlowNet(\n", + " pf=pf_estimator,\n", + " pb=pb_estimator,\n", + " off_policy=True, # No replay buffer.\n", + " init_logZ=0.0,\n", + ")\n", + "\n", + "# Magic hyperparameters: lr_base=4e-2, n_trajectories=3e6, batch_size=2048\n", + "train(lr_base=4e-2, # I started training this with 1e-3 and then reduced it.\n", + " n_trajectories=1e6,\n", + " batch_size=2048,\n", + " gradient_clip_value=10,\n", + " n_logz_resets = 0,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "u-ozqgc3hvSk", + "outputId": "28a2221c-2c5b-464e-eee7-1cd2e14199d6" + }, + "outputs": [ + { + "data": { + "image/png": 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iSBF0n0REyp5y8s9oESlPDh8+zNRp00hPT/d1UUREyhQFNxFxyciRI7FYLDz22GN53hs7diwWi4WRI0d6v2BXWLp0KTfddBPR0dFERkbSs2dPDhw44OtiiYiUioKbeF1mZibTp09n+vTpZGZm+ro4UoDC7lNsbCyLFi0iLS3NsS09PZ2FCxfSoEEDbxc1X99//z033XQTy5cvJy4ujuuvv56PPvqIEydO+LpoIiIlpuAmPpGVlUVWVpaviyFFKOg+de7cmdjYWJYuXerYtnTpUho0aECnTp2c9s3IyOCJJ56gVq1ahIWFcc0117B582anfZYvX06LFi0IDw9n3rx5JCUl5bnm+vXr6dOnD+Hh4cTGxvLEE0+QmppaYNnffvtt/vznP9OtWzeaN2/O9OnTqV69Ovv27XPxWxARKTsU3ESkRB588EHmzp3reP3BBx8watSoPPv9+c9/5n//+x/z589n69atNGvWjAEDBnDu3DkAEhISuOuuu7jtttuIj4+nc+fOfPPNN07nOHjwIDfffDN33303v/76K4sXL2b9+vWMGzeu2OW12WxkZmYSHh5ewk8sIuJ7Cm4iUiIPPPAA69ev58iRIxw5coQNGzbwwAMPOO2TmprKrFmzeO2117jlllto06YNc+bMITw8nPfffx+AWbNm0bRpU9544w1atmxJ+/bt6dixo9N5ZsyYwdChQxk/fjzNmzenV69e/P3vf+fDDz8s9gCG119/nczMTNq2beuWzy8i4guaDkRECmSzWThypAGLFwcQGwt9+uS8Fx0dzcCBA5k3bx6GYTBw4EBq1qzpdPzBgwfJysqid+/ejm3BwcF0796d3bt3A7B792569OjhdFz9+vWdXm/bto1ff/2VBQsWOLYZhoHNZuPQoUO0bt260M+xcOFCpk2bxvZ776VSpUoufQciImWJgpuI5Ouzzyy8/faTXLgQxfz55rb69aFZM4iKMl8/+OCDjubKmTNneqwsKSkpPProozzxxBN53itqMMSiRYt4+OGHWbJkCU2u6FsnIuJvFNxEJI+lS+GPfwzCMCKdth8/DseOQbdu5uubb76ZzMxMLBYLAwYMyHOepk2bEhISwoYNG2jYsCFgDnjYvHkz48ePB6B169Z88cUXTscdO3bM6XXnzp3ZtWsXzZo1c+lzfPTRRzz44IMsWrSIgQMHQu7gppUTRMQPqY+beJ3FYqFhw4Y0bNgQi8Xi6+LIFaxWePJJMAwA5/tjboMdO8z9AgMD2b17N7t27SIwMDDPuSpVqsSYMWOYNGkSK1asYNeuXYwePZpLly7x0EMPAfDYY4+xf/9+Jk2axN69e9m+fTvx8fFO53nmmWfYuHEj48aNIz4+nv379/P5558XOjhh4cKFDB8+nDfeeIMePXqQmJhISkqKJvUVEb+m4CZeFxwczMiRIxk5ciTBwcG+Lo5c4YcfzFq1K0Nbbmlp5n4AkZGRREZGFrjvK6+8wt13382wYcPo3LkzBw4cYOXKlVSrVg0wmzr/97//8dlnn9GhQwe2bNnCjTfe6HSO9u3bs27dOvbt20efPn3o1KkTL7zwAnXr1i3wuv/+97/Jzs5m7Nix1KlThzp16vD6G2+wYsWKYn8XIiJljcUw7P+GrhiOHTtGbGwsCQkJeTpAiwh89BEUZ4nShQthyBAPFODKJkx3Nmm6ci41pYqUKRcuXCAqKork5ORC/7FY3qnGTUSc1Knj3v1ERMR9FNzE6zIzM3nttdd47bXXtORVGdSnjzl6FPKvjLdYyDM1iIiIeIeCm/jEpUuXuHTpkq+LIfkIDIQ337RiBjfn8GYfS/L22+Z+IiLiXQpuIpJHWNhyAgP/SGTkBaft9esbfPIJ3HWXjwomIlLBaR43Ecnj/fffJyBgOePHX8WRIw345JMNpKbuZ/HiV+nZs7uviyciUmGpxk1EnCQnJ/PVV18BEBBg0LjxEW69NRlYx4oVy3xbOBGRCk7BTUScHDhwAKvVSu3atR3b7KsiLFum4CYi4ktqKhURJ0eOHAGc1wDt378/AHFxcZw8edIp1AmenXtORCQX1biJ11ksFurWrUvdunW15FUZZA9usbGxjvsUExPjWCd0z549viyeiEjxzJoF7dtDZKT56NkTvv465/30dBg7FmrUgMqV4e674eRJ53McPQoDB0JEBNSqBZMmQXa2dz/HFVTjJl4XHBzM6NGjfV0MKUDuGrfc9yk2NpYDBw7kWQC+QlCNmoj/qV8fXnkFmjc3F1qePx/uuAN++QXatoUJE2DZMliyBKKiYNw4c8j8hg3m8VarGdpiYmDjRjhxAoYPh+BgmD7dZx9LwU1EnNiDW8OGDZ2225eIS0hI8HqZCqRAJSIFue0259cvv2zWwv34oxnq3n/fXLvvhhvM9+fOhdatzfevvhpWrYJdu+Cbb6B2bejYEV56CZ55xvxdExLi7U8EqKlURK5QUHCLjY0FqJg1biLi36xWWLQIUlPNJtO4OMjKgn79cvZp1QoaNIBNm8zXmzZBu3ZmaLMbMAAuXICdO71b/lxU4yZel5WVxcyZMwEYO3YswcHBPi6R5GYPbvXq1ePtt98GzPtUJmvcRKTCuXDBeWLw0NBQQkND8995+3YzqKWnm/3YPv0U2rSB+HizxqxqVef9a9eGxETzeWKic2izv29/z0dU4yZeZxgGycnJJCcnYxj5r4cpvpGSksK5c+cAs49b7vukGjcRKQtiY2OJiopyPGbMmFHwzi1bmiHtp59gzBgYMcJs/vRjqnETEQd7bVvVqlWpUqWK03s+q3FTPzYRySUhIYHIyEjH6wJr28CsVbs8Ip4uXWDzZnjnHRg8GDIzISnJudbt5ElzMAKY//35Z+fz2Ued2vfxAQU3EXEoqH8b5PRxO336NOnp6YSFhXm1bGWKwqOIz0RGRjoFN5fYbJCRYYa44GBYs8acBgRg715z+o+ePc3XPXuaAxpOnTKnAgFYvdqcWqRNm9J/kBJScBMRh8KCW/Xq1QkPDyctLY3jx4/TtGlTbxevaKqdExG7yZPhllvMAQcXL5ojSNeuhZUrzek/HnoIJk6E6tXNMPb442ZYu/pq8/j+/c2ANmwYvPqq2a/tuefMud8Kq+XzMAU3EXE4fPgwAI0aNcrznsVioX79+uzfv5+EhISyGdxEROxOnTLnXTtxwgxq7duboe2mm8z333oLAgLMGreMDHPE6Lvv5hwfGAhffWX2jevZEypVMvvIvfiibz7PZQpuIuJQWI0bmM2l+/fv1wAFESn73n+/8PfDwmDmTPNRkIYNYfly95arlBTcxOssFgvR0dGO51J25A5u+d0nTQkiIuJbCm7idcHBwfzpT3/ydTEkH7mDW373SVOCiIj4loKbiACQkZHBiRMngMKbSsHHNW4acCAiFZgm4BURICeMhYeHU7NmzXz3UVOpiIhvqcZNvC4rK4s5c+YAMHr0aC15VUZc2b8tv/ukplIREd9ScBOvMwyD06dPO55L2XDliNL87pO9xu3MmTOkpaURHh7ug5J6iZpkRaQMUlOpiABFTwUCUK1aNSIiIgA4fvy4V8olIiI5FNxEBChecLNYLGVjgIKISAWl4CYiAPz+++9ATnNoQezvq5+biIj3KbiJCGD2WwMck+4WRDVuIiK+4/PgNnPmTBo1akRYWBg9evTg559/LnT/t99+m5YtWxIeHk5sbCwTJkwgPT3dS6UVKb/swa2gqUDsVOMmIuI7Ph1VunjxYiZOnMjs2bPp0aMHb7/9NgMGDGDv3r3UqlUrz/4LFy7k2Wef5YMPPqBXr17s27ePkSNHYrFYePPNN33wCaQkLBYLUVFRjudSNlwZ3Aq6T6pxExHxHZ8GtzfffJPRo0czatQoAGbPns2yZcv44IMPePbZZ/Psv3HjRnr37s39998PQKNGjRgyZAg//fSTV8stpRMcHMz48eN9XQzJ5dKlS6SlpQE5wa2g+1SvXj0AxyoLIiLiPT5rKs3MzCQuLo5+/frlFCYggH79+rFp06Z8j+nVqxdxcXGO5tTffvuN5cuXc+uttxZ4nYyMDC5cuOB4XLx40b0fRKQcsNe2hYSEULly5UL3rVGjBgBnz571eLlERMSZz2rczpw5g9VqpXbt2k7ba9euzZ49e/I95v777+fMmTNcc801GIZBdnY2jz32GH/5y18KvM6MGTOYNm2aW8suUt7kbiYtqvnaHtzsx4iIiPf4fHCCK9auXcv06dN599132bp1K0uXLmXZsmW89NJLBR4zefJkkpOTHY9du3Z5scSSH/tSSnPmzCErK8vXxRHyH5hQ0H2y75OSkkJmZqZ3CyoiUsH5rMatZs2aBAYGcvLkSaftJ0+eJCYmJt9jnn/+eYYNG8bDDz8MQLt27UhNTeWRRx7h//7v/wgIyJtDQ0NDCQ0Ndby+cOGCGz+FlIRhGI45w7TkVdmQX3Ar6D5FRUUREBCAzWbj7Nmz1KlTx7uFFRGpwHxW4xYSEkKXLl1Ys2aNY5vNZmPNmjX07Nkz32MuXbqUJ5wFBgYCCgAipVHcqUDA7ItavXp1p+NERMQ7fDqqdOLEiYwYMYKuXbvSvXt33n77bVJTUx2jTIcPH069evWYMWMGALfddhtvvvkmnTp1okePHhw4cIDnn3+e2267zRHgRMR1rgQ3MPu5nTlzRgMURES8zKfBbfDgwZw+fZoXXniBxMREOnbsyIoVKxwDFo4ePepUw/bcc89hsVh47rnnOH78ONHR0dx22228/PLLvvoIIuWCq8GtZs2a7N27V8FNRMTLfBrcAMaNG8e4cePyfW/t2rVOr4OCgpgyZQpTpkzxQslEKo6S1LiBpgQREfE2vxpVKiKeUdLgpj5uIiLe5fMaN6mYIiIifF0EyeX06dNA3uBW0H1SjZuIiG8ouInXhYSEMGnSJF8XQ3LJr8atsPtk30/BTUTEu9RUKlLBGYbhCG7R0dHFOkY1biIivqEaN5EK7sKFC2RnZwM5gawoftPHbepUX5dARMStFNzE67KysliwYAEAQ4cOJTg42Mclqtjs4atSpUqEh4c7thd2n0rdVHploFLAEhEpFgU38TrDMDhy5IjjufhWQSNKC7tPaioVEfEN9XETqeBcnQoEcoLb+fPnsVqtHimXiIjkpRo3kQquJMHNvlapYRicP3++6GPVFCoi4haqcROp4EoS3IKDg4mKigLUXCoi4k0KbiIVXEmCG6ifm4iILyi4iVRwpQ1uZX5KEBGRckR93MQnNAVI2VFYcCvsPqnGTUTE+xTcxOtCQkL4y1/+4utiyGUFBbei7pOWvRIR8T41lYpUcOrjJiLiPxTcRCo49XETEfEfaioVr8vOzubjjz8G4L777iMoSD+GvmK1Wjl37hyQN7gVdZ9U41aI3PPWaQ47EXEj/cUUr7PZbOzfv9/xXHwnKSnJcQ+uXGC+qPukPm4iIt6nplKRCszezBkVFeXySF/VuImIeJ+Cm0gFVtL+baA+biIivqDgJlKB2WvLrmwmLY7cTaWGYbi1XCIikj8FN5EK7Pz58wBUq1bN5WPtYS87O5uLFy+6tVwiIqU2YwZ06wZVqkCtWjBoEOzd67zPddeBxeL8eOwx532OHoWBAyEiwjzPpEmQne2tT5GHgptIBZaUlARA1apVXT42PDyc8PBwQM2lIlIGrVsHY8fCjz/C6tWQlQX9+0NqqvN+o0fDiRM5j1dfzXnPajVDW2YmbNwI8+fDvHnwwgte/Si5aVSpSAVmD24lqXEDs9bt2LFjnD17liZNmrixZCIipbRihfPrefPMGrO4OLj22pztEREQE5P/OVatgl274JtvoHZt6NgRXnoJnnnGnOonJMRDhS+Ygpt4XUhICFOmTPF1MYScptL8atyKc59q1qzpCG6lUp7nOrvys5XnzyriBRcuXHB6HRoaSmhoaNEHJieb/61e3Xn7ggXw3/+a4e222+D5580wB7BpE7RrZ4Y2uwEDYMwY2LkTOnUqxScpGTWVilRg7qhxA00JIiLeExsbS1RUlOMxY8aMog+y2WD8eOjdG666Kmf7/feboe2772DyZPjPf+CBB3LeT0x0Dm2Q8zoxsdSfpSRU4yZSgRVW41Yc9sBnD4AiIp6WkJBAZGSk43WxatvGjoUdO2D9euftjzyS87xdO6hTB268EQ4ehKZN3VRi91JwE6/Lzs7m008/BeDOO+/Uklc+VNjghOLcJ/tx9gAoIuJpkZGRTsGtSOPGwVdfwfffQ/36he/bo4f53wMHzOAWEwM//+y8z8mT5n8L6hfnYWoqFa+z2Wzs2rWLXbt2ackrHyusqbQ498ke3FTjJiJljmGYoe3TT+Hbb6Fx46KPiY83/1unjvnfnj1h+3Y4dSpnn9WrITIS2rRxe5GLQ1UdIhVYaZtKFdxEpMwaOxYWLoTPPzfncrP3SYuKgvBwszl04UK49VaoUQN+/RUmTDBHnLZvb+7bv78Z0IYNM6cJSUyE554zz12cJloPUI2bSAVWmnncch+n4CYiZc6sWeZI0uuuM2vQ7I/Fi833Q0LMaT7694dWreCpp+Duu+HLL3POERhoNrMGBpq1bw88AMOHw4sv+uQjgWrcRCqs3CselHRUqYKbiJRZRS3FFxtrTtJblIYNYfly95TJDVTjJlJBJdvnNAKioqJKdA4FNxER71JwE6mg7GGrUqVKBAcHl+gcmg5ERMS7FNxEKqjSTr4LqnETEfE29XETrwsODmby5MmO5+IbRY0oLc59yh3cDMPAYrG4vZwiIpJDwU28zmKxEOKDhXnFWVEjSotzn+zHWq1WUlNTqVy5shtLKCIiV1JTqUgF5Y6m0vDwcEdtnJpLRUQ8T8FNvC47O5vPPvuMzz77jOzsbF8Xp8Iqqqm0OPfJYrGon5uIiBcpuInX2Ww2tm3bxrZt27TklQ8V1VRa3Puk4CYi4j0KbiIVlDuaSkHBTUTEmxTcRCqo0q5Tamc/3n4+ERHxHAU3kQqqtOuU2qnGTUTEexTcRCooNZWKiPgfBTeRCsrdTaUKbiIinqcJeKVCWbt2Lddffz3nz58vdWDxd6px85GpUwt/LSJSCNW4idcFBwfz9NNP8/TTTzstpTRy5EgsFguPPfZYnmPGjh2LxWJh5MiRXixp/k6cOMH9999PixYtCAgIYPz48b4uUokU1cetoPt0JQU3ERHvUXATr7NYLFSqVIlKlSrlWdsyNjaWRYsWkZaW5tiWnp7OwoULadCggbeLmq+MjAyio6N57rnn6NChg6+LUyLp6emkp6cDhS95VdB9yk3BTUTEexTcpEzp3LkzsbGxLF261LFt6dKlNGjQgE6dOjntm5GRwRNPPEGtWrUICwvjmmuuYfPmzU77LF++nBYtWhAeHs7111/P4cOH81xz/fr19OnTh/DwcGJjY3niiSdITU0tsIyNGjXinXfeYfjw4URFRZXuA/uIPWQFBARQpUqVUp1LwU1ExHsU3MTrsrOzWbZsGcuWLct3KaUHH3yQuXPnOl5/8MEHjBo1Ks9+f/7zn/nf//7H/Pnz2bp1K82aNWPAgAGcO3cOgISEBO666y5uu+024uPjefjhh3n22WedznHw4EFuvvlm7r77bn799VcWL17M+vXrGTdunJs/ddliD1lRUVEEBOT/a6Co+2Sn4CYi4j0KbuJ1NpuNLVu2sGXLlnyXUnrggQdYv349R44c4ciRI2zYsIEHHnjAaZ/U1FRmzZrFa6+9xi233EKbNm2YM2cO4eHhvP/++wDMmjWLpk2b8sYbb9CyZUuGDh2ap4/cjBkzGDp0KOPHj6d58+b06tWLv//973z44YeOpsTyqDgjSou6T3YKbiIi3qNRpeJ1ViscOtSQlJQqrFtn4YYbIDAw5/3o6GgGDhzIvHnzMAyDgQMHUrNmTadzHDx4kKysLHr37u3YFhwcTPfu3dm9ezcAu3fvpkePHk7H9ezZ0+n1tm3b+PXXX1mwYIFjm2EY2Gw2Dh06ROvWrd31scsUd40oBefgZhhGof3hRESkdBTcxKuWLoUnngjm+PGRAPzvf1C/PrzzjvN+Dz74oKO5cubMmR4rT0pKCo8++ihPPPFEnvfKymAIT3DXqgmQE/5sNhspKSml7jNX4eSeDkRTg4hIEdRUKl6zdCnccw8cP+68/fhxc/uRIznbbr75ZjIzM8nKymLAgAF5ztW0aVNCQkLYsGGDY1tWVhabN2+mTZs2ALRu3Zqff/7Z6bgff/zR6XXnzp3ZtWsXzZo1y/MICQkp5Scuu9w1+S5AWFiY47tSc6mIiGcpuIlXWK3w5JNgGADOTWnmNvj555zngYGB7N69m127dhGYux31skqVKjFmzBgmTZrEihUr2LVrF6NHj+bSpUs89NBDADz22GPs37+fSZMmsXfvXhYuXMi8efOczvPMM8+wceNGxo0bR3x8PPv37+fzzz8vcnBCfHw88fHxpKSkcPr0aeLj49m1a1dJvhqfcGdTqcVi0ULzIiJeoqZS8YoffoBjxwp+3zDg0iU4ezZnW2RkZKHnfOWVV7DZbAwbNoyLFy/StWtXVq5c6QgjDRo04H//+x8TJkzgH//4B927d2f69Ok8+OCDjnO0b9+edevW8X//93/06dMHwzBo2rQpgwcPLvTauacmiYuLY+HChTRs2DDf6UbKInfWuNnPc+rUKdW4iYh4mIKbeMWJE8XZax5jxxb87meffeb0OiwsjL///e/8/e9/L/CYP/zhD/zhD39w2nbl1CLdunVj1apVxSmgg2GvGvRT7uzjlvs8Cm4iIp6l4CZeUaeOe/eT0ilOU2lwcDBPPvmk43lhFNxERLyjRH3cDh48yHPPPceQIUM4deoUAF9//TU7d+50a+Gk/OjTByIjLwAFzQdmo359gz59vFmqiqs4TaX2vmtVq1YtcooPBTcREe9wObitW7eOdu3a8dNPP7F06VJSUlIAcz6sKVOmuL2AUj4EBkKNGi9efnVlM6MZ5oYO3Uw+4xDEA9RUKiLin1wObs8++yx//etfWb16tdN0CTfccEOeqRZE7BISEjh06A0slvuoU8e51q1y5WTgHo4dK7ivmrhXcZpKrVYrq1atYtWqVVit1kLPp+AmIuIdLge37du3c+edd+bZXqtWLc6cOeOWQkn588UXXwDQu/dJDh60MmLEPO6++3+sWpXF11/vAT7liy++KNfLTJUludcqLYjVamXTpk1s2rRJwU1EpIxwObhVrVqVE/kMEfzll1+oV6+eWwol5Y89uN1+++0EBkLjxkdo124Hffsa9OrVg/r163Px4kVWrlzp45KWf4ZhkJycDKipVETE37gc3P74xz/yzDPPkJiYiMViwWazsWHDBp5++mmGDx/ucgFmzpxJo0aNCAsLo0ePHnlmur9SUlISY8eOpU6dOoSGhtKiRQuWL1/u8nXFey5cuMB3330HmMHtSgEBAdx7770AfPzxx14tW0V06dIlsrOzgcJr3Fyh4CYi4h0uB7fp06fTqlUrYmNjSUlJoU2bNlx77bX06tWL5557zqVzLV68mIkTJzJlyhS2bt1Khw4dGDBggGOk6pUyMzO56aabOHz4MJ988gl79+5lzpw5qukr41auXElWVhYtW7akZcuW+e5jD25ffPEFaWlp3ixehWOvbQsMDKRSpUpuOaeCm4iId7g8j1tISAhz5szh+eefZ8eOHaSkpNCpUyeaN2/u8sXffPNNRo8e7ZgQdfbs2SxbtowPPviAZ599Ns/+H3zwAefOnWPjxo2OeaUaNWrk8nXFuz7//HMg/9o2ux49ehAbG0tCQgJr1qzJM2muuI89uEVGRhY5zUdx5QluWixdRMQjSrxWaYMGDbj11lu57777ShTaMjMziYuLo1+/fjmFCQigX79+bNq0Kd9jvvjiC3r27MnYsWOpXbs2V111FdOnTy+043RGRgYXLlxwPC5evOhyWaXksrOzHU3ZhQU3+72HvAvBi3u5u3+b/VxTgEdPnFBoExHxIJdr3HKv85ifDz74oFjnOXPmDFarldq1azttr127Nnv27Mn3mN9++41vv/2WoUOHsnz5cg4cOMCf/vQnsrKyCpxDbsaMGUybNq1YZRL327ZtG+fPn6dq1ar07Nmz0H27devG3Llz2bJli5dKVzEVZ0Spq+whMD09HcMw3FaTJyIizlwObvYZ1+2ysrLYsWMHSUlJ3HDDDW4rWH5sNhu1atXi3//+N4GBgXTp0oXjx4/z2muvFRjcJk+ezMSJEx2vjx8/Tps2bTxaTsmxdetWALp27Urg5dl1g4ODGTNmjOO5Xbdu3QDYvHmz/vh7kL3GrajgVtB9yo/9XAZmbXpoaGjpCyoiInm4HNw+/fTTPNtsNhtjxoyhadOmxT5PzZo1CQwM5OTJk07bT548SUxMTL7H1KlTh+DgYEcAAGjdujWJiYlkZmY6TQhsFxoa6vRH5MKFC8Uuo5SePbh17tzZsc1isVCrVq08+7Zr146QkBDOnTvHoUOHaNKkidfKWZEUN7gVdJ/yEx4eToDFgs0wSE9PV3ATEfGQEvdxczpJQAATJ07krbfeKvYxISEhdOnShTVr1ji22Ww21qxZU2CTWu/evTlw4AA2W87M+/v27aNOnTr5hjbxvfyCW0FCQ0Np3749gJpLPcjdy12BGfLCwsIAs1+piIh4hluCG5gLz9vnhiquiRMnMmfOHObPn8/u3bsZM2YMqampjlGmw4cPZ/LkyY79x4wZw7lz53jyySfZt28fy5YtY/r06YwdO9ZdH0PcKCsri23btgHQpUsXx3ar1cratWtZu3ZtnoEluZtLxTOKW+NW2H3Kjz24afULERHPcbmpNHd/MTBnYT9x4gTLli1jxIgRLp1r8ODBnD59mhdeeIHExEQ6duzIihUrHAMWjh49SkBATraMjY1l5cqVTJgwgfbt21OvXj2efPJJnnnmGVc/hnjBnj17yMjIIDIy0qnZ02q1sm7dOgB69erl1PTdrVs3Zs2apeDmQa4Et4LuU37szaMKbiIinuNycPvll1+cXgcEBBAdHc0bb7xR5IjT/IwbN45x48bl+97atWvzbOvZs6emi/ATcXFxAHTq1MkpgBema9eujmOtVmuRYUFcV9zg5io1lYqIeJ7Lwc2+dJFIUVzp32bXunVrIiIiSElJYd++fbRu3dpTxauwPNHHDVTjJiJlzIwZsHQp7NkD4eHQqxf87W+QewWf9HR46ilYtAgyMmDAAHj3Xcg9VdnRozBmDHz3HVSuDCNGmOcOcjlCuYXb+riJXMke3HL3bytKUFCQI+ipudQzVOMmIhXCunUwdiz8+COsXg1ZWdC/P6Sm5uwzYQJ8+SUsWWLu//vvcNddOe9brTBwIGRmwsaNMH8+zJsHL7zg9Y9jV6y42KlTp2LPqWX/Yy0Vm9VqJT4+HnCtxg3M5tL169ezefNmhg8f7oHSVWyeDm6qcRORMmHFCufX8+ZBrVoQFwfXXgvJyfD++7BwIdjnoZ07F1q3NsPe1VfDqlWwaxd8841ZC9exI7z0EjzzjLlKjA9mtChWcBs0aJCHiyHlzf79+0lNTSUiIoIWLVq4dKx9ZKmmBPEMTwU3NZWKiDdcOR/rlfO1Fujy7z6qVzf/Gxdn1sLlWnqTVq2gQQPYtMkMbps2Qbt2zk2nAwaYTac7d0KnTkVfNyEBLBaoX998/fPPZlhs0wYeeaTo469QrOBW0KoEIgWxD0zo2LGjywMM7AMU4uPjsdlsxR7YIMXjqT5uaioVEW+IjY11ej1lyhSmFrVGss0G48dD795w1VXmtsREs8bsyt+FtWub79n3uWJpTsdr+z5Fuf9+M6ANG2Yec9NN0LYtLFhgvnax2dU3Peuk3Cusf1tQUBAPP/yw4/mVmjRpQkhICOnp6Rw9epRGjRp5tKwVic1mc/xrtagat6Lu05XUVCoi3pCQkEBkZKTjdbFq28aOhR07YP16D5asADt2QPfu5vOPPzaD44YNZjPsY495PrhZrVbeeustPv74Y44ePUpmZqbT++fOnXP1lFIO2Sfe7ZRPNXJAQAD16tUr8NigoCCaN2/Ozp072bNnj4KbG6WkpGAYBlB0cCvqPl3J/stTNW6lcGWtQVG1CCIVUGRkpFNwK9K4cfDVV/D99znNlQAxMeagg6Qk51q3kyfN9+z7/Pyz8/nsS3UWsDxnHllZYA+X33wDt99uPm/VCk6cKP7nuMzlNqhp06bx5ptvMnjwYJKTk5k4cSJ33XUXAQEBRVdVSoWxZ88egBJP59GqVSun84h72Pu3BQcHO2rI3EU1biJSphiGGdo+/RS+/RYaN3Z+v0sXCA6GXEtvsnevOf2HfenNnj1h+3Y4dSpnn9WrITLS7KNWHG3bwuzZ8MMP5rE332xu//13qFHD5Y/lcnBbsGABc+bM4amnniIoKIghQ4bw3nvv8cILL2hiXAHMWp3jx48D0DL3fDmXWa1WNmzYwIYNGwpcSknBzTNy928raqR4ce5TbhqcICJlytix8N//mgMBqlQx+5MlJkJamvl+VBQ89BBMnGjO0RYXB6NGmWHt6qvNffr3NwPasGGwbRusXAnPPWeeuzhNtGDOHfevf8F118GQIdChg7n9iy9ymlBd4HJTaWJiIu3atQOgcuXKjn/B/+EPf+D55593uQBS/uzbtw+A6OhoqlWrlud9q9XKN998A5gjSPMbvKDg5hmujCgtzn3KTYMTRKRMmTXL/O911zlvnzsXRo40n7/1FgQEwN13O0/AaxcYaDazjhljBrpKlcwJeF98sfjluO46OHMGLlyA3H8TH3kEIiJc/lguB7f69etz4sQJGjRoQNOmTVm1ahWdO3dm8+bNxesgKOXe3r17gfxr24pLwc0zPDUVCOQKbpmZGg0sIr53uT9vocLCYOZM81GQhg1h+fLSlyUuDg4eNEeZVqlijmgtQXBz+TfrnXfeyZrL7cGPP/44zz//PM2bN2f48OElWqtUyh93BDf7sSdPnuT8+fNuKZfkNJV6Irjl/ofblYOWREQqrCNHzLng7rjDbGI9fdrc/re/wdNPu3y6Yte4/fOf/+SBBx7glVdecWwbPHgwDRo0YNOmTTRv3pzbbrvN5QJI+eOO4FalShXq1avH8ePH2bt3L1fb+xtIqdhr3Nwyh9sVg5GCgoIICgwk22olPT3d7YMfKiSNMhXxf08+CV27mn3kcg9GuPNOGD3a5dMVu8bt//7v/6hbty5Dhw7l22+/dWzv2bMnEydOVGgTB3cEN1BzqSd4sqkUNLJURCSPH34wBzRcuTxWo0ZweSCfK4od3BITE5k9eza///47N910E40bN+all14iISHB5YtK+WUYhmNwgoJb2ePp4Ka53ERErmCzmYvVX+nYMbOvm4uKHdzCw8MZPnw43333Hfv372fYsGG8//77NG7cmJtvvpklS5aQlZXlcgGkfDl+/DipqakEBgbS+Mo5c1yk4OZ+nuzjBqpxExHJo39/ePvtnNcWC6SkwJQpcOutLp+uREteNWnShBdffJFp06bxzTffMG/ePEaOHEmlSpU4lXuSOqlw7M2k9mWr8hMUFMSIESMczwui4OZ+rtS4Ffc+5aa53ERErvDGG+Y0I23aQHq6Oap0/36oWRM++sjl05VqrVKLxUJQUBAWiwXDMFTjJsVqJg0ICCjWMlb24Hbw4EGysrIIDg52SxkrMlcGJxT3PuWmudxERK5Qv745MGHRIvj1V7O27aGHYOhQCA93+XQlCm4JCQnMnTuXefPmcfToUa699lrmzJnD3XffXZLTSTniroEJAPXq1aNSpUqkpqZy8OBBR5CTktPgBBERHwgKggcecM+pirtjZmYmS5cu5YMPPuDbb7+lTp06jBgxggcffJAmTZq4pTDi/4oT3KxWK3FxcQB06dKlwBn5LRYLrVq1Ii4ujj179ii4uYErfdyKe59y0+AEERHM5ayKy77ofDEVO7jFxMRw6dIl/vCHP/Dll18yYMAAzYwueRQ3uH399dcAdOzYsdBA0Lp1a+Li4ti9ezeDBg1ya1krIleXvCrufbJTjZuICFDcv1cWS/4jTgtR7OD23HPPMWzYMKKjo126gFQc6enpHD58GHBPUylA8+bNAbOfm5SeWyfgzYeCm4gI5hQgHlLs4DZx4kSPFULKhwMHDmAYBlFRUdSqVcst57Q3w//2229uOV9Flp2dTUpKClDCPm7FmLVfTaUiIp6ltk5xG/uI0hYtWmCxWNxyTgU397lw4YLjuQYniIh40Zo18Ic/QNOm5uMPf4BvvinRqRTcxG3s4apZs2ZuO6c9uCUkJGjh8lKyN5OGhYUVOMdeaanGTUTkCu++CzffbK6S8OST5iMy0px8d+ZMl09XqnncRHI7dOgQQKlXTMitdu3ahIeHk5aWxpEjRxx93sR1nu7fBqpxExHJY/p0eOstGDcuZ9sTT0Dv3uZ7Y8e6dDrVuInb2Gvc3Dk9jMViUXOpm3h6uStQcBMRySMpyaxxu1L//nD5H9SuKFaNmysDE958802XCyHlQ3Fr3IKCghgyZIjjeVGaNm3Kzp07FdxKydXJd129T5DTVJqVnY3Vai3WFCIiIuXa7bfDp5/CpEnO2z//3Ozr5qJi/Tb+5ZdfnF5v3bqV7Oxsx5QP+/btIzAwkC5durhcACkfbDabI7gVVeMWEBBAixYtin1u1bi5h6vBzdX7BDnBDcx+bhERES4dLyJS7rRpAy+/DGvXQs+e5rYff4QNG+Cpp+Dvf8/Z94knijxdsYLbd99953j+5ptvUqVKFebPn0+1atUAOH/+PKNGjaJPnz7F/yBSrpw4cYLMzEwCAwOpX7++W8+t4OYe3ujjFhgYSEhwMJlZWaSnpyu4iYi8/z5Uqwa7dpkPu6pVzffsLBb3Bbfc3njjDVatWuUIbQDVqlXjr3/9K/379+epp55y9ZRSDthDVcOGDYtsVrNarWzfvh2Adu3aFdmcpuDmHq72cctzn4p5ndDQUDKzsjSyVEQE4HJrlLu4HNwuXLjA6dOn82w/ffo0Fy9edEuhxP+4MqLUarXy+eefA9CmTRuXgpthGG6bI66icbXGLc99KuZ1wsLCuJiSogEKIiIe4HJwu/POOxk1ahRvvPEG3bt3B+Cnn35i0qRJ3HXXXW4voPgHT4wotWvUqBFg/qPh3Llz1KhRw+3XqAjsNW6ebCoFzeUmIuLEMOCTT+C77+DUqbzLYS1d6tLpXA5us2fP5umnn+b+++8nKyvLPElQEA899BCvvfaaq6eTcsITc7jZhYeHU7duXX7//Xd+++03BbcS8sZ0IKApQXzmyiXJirFEmYh4wfjx8K9/wfXXQ+3aZl+2UnApuFmtVrZs2cLLL7/Ma6+95lj4u2nTplSqVKlUBRH/5skaN/t57cGtW7duHrlGeeeNwQmg4CYi4uQ//zFr1W691S2nc2kC3sDAQPr3709SUhKVKlWiffv2tG/fXqFNPFrjBhqg4A5qKhUR8YGoKHBjpYbLTaVXXXUVv/32m8f+QIv/SU9P5/jx44Bna9xAwa00ShLc+l6eCigwOxuKOZmuatxERHKZOhWmTYMPPoDw8FKfzuXg9te//pWnn36al156iS5duuSpbYuMjCx1ocS/HDlyBIDKlSt7rP+ZglvpqY+biIgP3HcffPQR1KoFjRpBcLDz+1u3unQ6l4PbrZfbaG+//XanaRns0zRYrVZXTyl+Lnf/tuJM1REUFMQ999zjeF4cCm6lYxiGy33cgoKCaNOmDWCuolBcCm4iIrmMGAFxcfDAA94fnADOqyiIgOv92wICAmjbtq1L17AHt6NHj5KVlUXwlf9ikUKlpaU5RoEXN7gFBARQq1Ytl6+l4CYiksuyZbByJVxzjVtO53Jw69u3r1suLOWHp0eUAsTExBAeHk5aWhpHjhyhWbNmHrtWeWRvJg0ICKBy5coevZY9uGlwgogIEBsLbuxG5nJws7t06RJHjx4lMzPTaXv79u1LXSjxL67WuNlsNnbv3g1A69ati9UMZ7FYaNiwIXv27FFwK4HcC8wXd+UJm83GmVOnAIiOji72capx8yDN1Sbif954A/78Z5g92+zjVkouB7fTp08zatQovv7663zfVx+3isfVGrfs7Gw++eQTACZPnkxISEixjssd3MQ1JRlRmp2dza7LCyL36dOnyKXJ7BTcRERyeeABuHQJmjaFiIi8gxPOnXPpdC4Ht/Hjx5OUlMRPP/3Eddddx6effsrJkyf561//yhtvvOHq6aQc8PQcbnYNGzYEUHArAW/N4QbOwU1ry4pIhff22249ncvB7dtvv+Xzzz+na9euBAQE0LBhQ2666SYiIyOZMWMGAwcOdGsBpWxLTk52NMPZg5WnKLiVnC+Cm80wyMrKKnaNqohIuTRihFtP53JwS01NdYw0q1atGqdPn6ZFixa0a9eOrS7ORSL+zx6iatSo4fEVNOyLzR8+fNij1ymPCpwKxAN9poKCgggMCMBqs5Genq7gJiJil54OV4wNcHXggktLXgG0bNmSvXv3AtChQwf+9a9/cfz4cWbPnk2dOnVcPZ34OXtw83RtW+5rqMbNdd6afBfMgSTq5yYicllqKowbZ07AW6kSVKvm/HCRy8HtySef5MSJEwBMmTKFr7/+mgYNGvD3v/+d6dOnu1wA8W++CG7Hjh3TIBgXebOpFDRAQUTE4c9/hm+/hVmzIDQU3nvPXAKrbl348EOXT+dyU+kDDzzgeN6lSxeOHDnCnj17aNCgATVr1nS5AOLfvBnc6tSpQ3BwMFlZWfz+++/ExsZ6/JrlRbGDW66m0sBShGMFNxGRy7780gxo110Ho0ZBnz7QrBk0bAgLFsDQoS6dzuXg9ttvvzlN+xAREUHnzp1dPY2UEyUJboGBgdxxxx2O564cFxsby2+//cbhw4cV3Fzg6nJXYDZ5tmrVyvHcFQpuIiKXnTsH9twUGZkz/cc118CYMS6fzuWm0mbNmtGgQQOGDRvG+++/z4EDB1y+qJQf9oECrga3jh070rFjR5eCW+7rqJ+ba0rSxy0gIICYmBhiYmJcWqsUFNxERByaNIHL02bRqhV8/LH5/MsvoQTdV1wObgkJCcyYMYPw8HBeffVVWrRoQf369Rk6dCjvvfeeywUQ/+bNplLIGVmq4OYa9XETEfGRUaNg2zbz+bPPwsyZEBYGEybApEkun87l4FavXj2GDh3Kv//9b/bu3cvevXvp168fH3/8MY8++qjLBRD/lZaWxqnLSyK5EtxsNhv79u1j37592Gw2l66pGreSKUlwMwyDs2fPcvbsWQzDcOl6Cm4iUiZ8/z3cdps5EMBigc8+c35/5Ehze+7HzTc773PunNkPLTLSrCF76CFISSl+GSZMgCeeMJ/36wd79sDChfDLL/Dkky5/JJeD26VLl1i1ahV/+ctf6NWrF+3bt2fbtm2MGzeOpUuXulwA8V9Hjx4FoFKlSlSvXr3Yx2VnZ/PRRx/x0UcfkZ2d7dI17cFNc7m5piR93Gw2G9u3b2f79u0uB2wFNxEpE1JToUMHs5arIDffDCdO5Dw++sj5/aFDYedOWL0avvrKDIOPPFL0tTdtMvfPzT5I4bHH4J//hIwMlz+Sy4MTqlatSrVq1Rg6dCjPPvssffr0oVoJ5iER/5e7mdRbyxqpxq1kvDmPGyi4iUgZccst5qMwoaEQE5P/e7t3w4oVsHkzdO1qbvvHP+DWW+H1182avIK8+KIZ0v7wB/P19u1mbd3IkdCmDbz6qnm8ixOfu1zjduutt2K1Wlm0aBGLFi1iyZIl7Nu3z9XTSDng7f5tkNPH7ejRoy4331VUGRkZpKWlAerjJiKSx9q15uS4LVuaozzPns15b9Mms3nUHtrAbO4MCICffir8vPHxcOONOa8XLYIePWDOHLP59O9/zxmo4AKXg9tnn33GmTNnWLFiBT179mTVqlX06dPH0fdNKg5fBLf69esTEBBAeno6J0+e9Np1/Zm9mRQg0sWlVUoqNDQUUHATEfe7cOGC0yOjBM2NDjffbDZfrlkDf/sbrFtn1tDZ57FMTDRDXW5BQVC9uvleYc6fh9q1c17bz23XrRskJLhcZJeDm127du3o3bs3PXv2pFu3bpw6dYrFixeX9HTih3wR3IKDg6l7uWpazaXFYw9ukZGRLk+/UlKqcRMRT4mNjSUqKsrxmDFjRslP9sc/wu23Q7t2MGiQ2Sdt82azFq60atfOmQYkMxO2boWrr855/+JFCA52+bQuB7c333yT22+/nRo1atCjRw8++ugjWrRowf/+9z9Onz7tcgHEf/kiuIGmBHGVt/u3gYKbiHhOQkICycnJjsfkyZPdd/ImTaBmTbDPURsTA5dnT3DIzjZHmhbUL87u1lvN6T9++AEmT4aICHPVBLtff4WmTV0uosuDEz766CP69u3LI488Qp8+fbz6x0DKFl8Ft4YNG7J+/XoFt2Ly9hxu4BzcDMPw2uAVESn/IiMjPdft49gxs49bnTrm6549ISkJ4uKgSxdz27ffgs1m9lcrzEsvwV13Qd++ULkyzJ8PISE573/wAfTv73IRXQ5umzdvdvkiUv5kZ2dz/PhxwPXgFhgYyC2X2/lL0nSnKUFcU9LgZrFYaN68ueO5K+zBzWYYZGVlEZL7l5WIiLekpOTUnoHZdBkfb/ZRq17dXOz97rvN2rODB80F4Zs1gwEDzP1btzb7wY0eDbNnQ1YWjBtnNrEWNqIUzJq777+H5GQzuF35927JEnO7i1wObgA//PAD//rXvzh48CCffPIJ9erV4z//+Q+NGzfmmmuuKckpxc8cP34cq9VKcHAwdez/MimmwMBAunfvXuJra0oQ15RkDjcwl7yqV69eia4ZHBxMgMWCzTBIT09XcPMUF6cREKlwtmyB66/PeT1xovnfESNg1iyzuXL+fLNWrW5dswbspZfMKULsFiwww9qNN5qjSe++2xwRWlwFtUy6MP9pbi4Ht//9738MGzaMoUOH8ssvvzhGcyQnJzN9+nSWL19eooKIf7GHpgYNGri8jmVp2YObfQJgKZwv+rhZLBbCwsK4lJZGenq610aziog4ue46KGzqqJUriz5H9ermSgdlhMt/cf/6178ye/Zs5syZQ3Cu0RC9e/dm69atJSrEzJkzadSoEWFhYfTo0YOff/65WMctWrQIi8XCoEGDSnRdKbnS9G+z2WwcPnyYw4cPuzwjf+5rqsateEraVGoYBklJSSQlJZVozjwNUBARcT+Xg9vevXu59tpr82yPiopy/IFwxeLFi5k4cSJTpkxh69atdOjQgQEDBjjWwCzI4cOHefrpp+mTe4SGeE1pglt2djbz589n/vz5Li95BeZQcDDn8sk9R5nkr6RNpTabjfj4eOLj40sUsBXcRETcz+XgFhMTw4HcHf0uW79+PU2aNHG5AG+++SajR49m1KhRtGnThtmzZxMREcEHH3xQ4DFWq5WhQ4cybdq0El1TSs9XI0rBXBu1Zs2aTuWQgvliVCkouImIeILLwW306NE8+eST/PTTT1gsFn7//XcWLFjA008/zZgxY1w6V2ZmJnFxcfTr1y+nQAEB9OvXj02bNhV43IsvvkitWrV46KGHirxGRkaG0wzLFy9edKmMkj97/7IGDRr45Pr266qfW9F80ccNFNxERDzB5cEJzz77LDabjRtvvJFLly5x7bXXEhoaytNPP83jjz/u0rnOnDmD1Wqldu4lIYDatWuzZ8+efI9Zv34977//PvHx8cW6xowZM5g2bZpL5ZKilYXgtnXrVtW4FYNq3CqoK0ecagSqSLngco2bxWLh//7v/zh37hw7duzgxx9/5PTp07z00kuOhaw95eLFiwwbNow5c+Y4msqKMnnyZKcZlnft2uXRMlYEhmH4tKk093VV41a0kvZxKy0FNxER9yvRPG4AISEhtGnTBjCbI998801effVVEotadDWXmjVrEhgYmGex8JMnTxKTz1ISBw8e5PDhw9x2222ObfZO00FBQezdu5emVywfERoa6ljwGswO7VI6Z8+edYT0+vXr+6QMaiotPtW4iYiUH8WuccvIyGDy5Ml07dqVXr168dlnnwEwd+5cGjduzFtvvcWECRNcunhISAhdunRhzZo1jm02m401a9bQs2fPPPu3atWK7du3O0a6xcfHc/vtt3P99dcTHx/vGG0onmWvbYuJiXH8cfY2TQlSfOrjJiJSfhS7xu2FF17gX//6F/369WPjxo3ce++9jBo1ih9//JE333yTe++9t0TLF02cOJERI0bQtWtXunfvzttvv01qaiqjRo0CYPjw4dSrV48ZM2YQFhbGVVdd5XS8vRbhyu3iOaXt3xYYGOgYkFKSn5nc11aNW+GsVqtjQE5Jlryyj9ouyVqjCm4iIu5X7OC2ZMkSPvzwQ26//XZ27NhB+/btyc7OZtu2baVaQHrw4MGcPn2aF154gcTERDp27MiKFSscAxaOHj3q9Zn5pXCl7d8WGBhI7969S1UGe3D7/fffyczM1JJKBcg9z52rNW4BAQGlGnyi4CYi4n7FDm7Hjh2jS5cugFm7FRoayoQJE0oV2uzGjRvHuHHj8n1v7dq1hR47b968Ul9fXOPrEaUAtWrVIjQ0lIyMDI4fP07jxo19Vpay7Pz584A59523w62Cm4iI+xW7KstqtTr94g8KCqJyCVa1F/9X2ho3m83G8ePHOX78eIlm5Aez6c4eHNXPrWD24Fa9BIsZG4bhmP9QS16JiJQNxa5xMwyDkSNHOkZopqen89hjj1GpUiWn/ZYuXereEkqZU9oat+zsbN577z3AnK6lpDVBDRs2ZP/+/ernVohz584BUK1aNZePtdlsjvWH+/Tp43J/xNzBzTAMt9TOi4hUdMUObiNGjHB6/cADD7i9MOIf7DVcvmwqzX191bgVzB7cSlLjVlr24GYzDLKzswkODvZ6GUREyptiB7e5c+d6shziJ9LS0jh9+jTgu8l37TSytGilaSotreDgYAIsFmyGQXp6uoKbiIgbaLimuCQhIQEwO7uXpPnNnbR6QtFK01RaWhaLxVHr5ulVVUREKgoFN3FJ7oEJvu6zpKbSovmyqRQgPDwcUHATEXEXBTdxSVmYCsQud41bSUY9VgS+bCoFBTcREXdTcBOX+Hpx+dzs66SmpaVx5swZH5embPJlUykouImIuFuJF5mXiskdNW6BgYH07dvX8bykQkNDiYmJITExkaNHjxIdHV3ic5VXpWkqtVgsNGrUyPG8JBTcRETcSzVu4hJ31LgFBgZy3XXXcd1115UquOUuh/q55e/8+fNMAbp8+SVMnerSsQEBATRq1IhGjRqVeNk5BTcREfdScBOXlKU+bqABCkWx17jZA5S3KbiJiLiXmkql2Gw2m2M6kNLUuBmG4ZgLLjo6ulSjU+1NeQpu+StNcDMMg0uXLgEQERFRovuk4CYi4l6qcZNiS0xMJCsri4CAAOrWrVvi82RlZTFr1ixmzZpFVlZWqcqkptKCpaWlOdYJLUlws9lsbN68mc2bN5d4TVkFNxER91Jwk2Kzh6P69esTFFQ2KmsV3ApmnwokwGIp8XqwpaXgJiLiXmXjr6/4hcOHDwNlYyoQOwW3gtmbScPCwnw2WbKCWxmSe3CKiwNVRKTsUI2bFFtZmsPNzl6Wc+fOkZKS4uPSlC32GjdfDUzIfW0FNxER91Bwk2KzBzf7gICyIDIykqpVqwKqdbuSr0eU5r52RmYmVqvVZ+UQESkv1FQqxVYWa9zALE9SUhJHjhyhbdu2vi5OmZFvcPNyE5l9kXmA9PR0KlWq5NXri4iUNwpuUmxlsY8bmOXZtm2batzsLoezVps2MQXn8ORtAQEBhIWGkp6RQVpamoKbiEgpKbhJsRiG4bam0sDAQHr27Ol4XloaoJA/e7+ykjaVWiwWYmNjHc9LKjw83BHcRESkdBTcpFjOnj3rmIzV/se8pAIDA+nfv787igUouBWktMEtICCApk2blroc4eHhnE9KUnATEXEDDU6QYrGHopiYGJ82veXHHtzsTbliKm1wcxeNLBURcR/VuEmxuLN/m2EYJCcnAxAVFVXqOcZU45a/0gY3wzDIyMgAIDQ0tMT3ScGtDLpykIrmdRPxG6pxk2Jx51QgWVlZvPPOO7zzzjulXvIKcoLbiRMnHEFDSh/cbDYbP/74Iz/++GOJl7zKfX0FNxGR0lNwk2Ipq1OBgLlQvT0cJCQk+Lg0ZYd9nVJfN20ruImIuI+aSqVYynJws1gsNGzYkD179nDkyBGaNWvm6yKVCerjJsWmplMRv6EaNykWex+3srRqQm7q5+bMMAxHjZuCm4hI+aHgJsVSlmvcQMHtSunp6RiXnyu4iYiUHwpuUqTk5GSSkpIABTd/YQ9JIcHBbpnkuDQU3ERE3EfBTYpkD0PVq1encuXKPi5N/hTcnJWV/m25y6DgJiJSehqcIEVy51QgYM7I37VrV8dzd1Bwc+aOEaUWi4W6des6npeUPbilp6djs9ncds9FRCoi/QaVIrm7f1tQUBADBw5k4MCBBAW5598O9lCZkJBAdna2W87pz9xR4xYQEECLFi1o0aJFqcKWvQwGaJ49EfGu77+H226DunXBYoHPPnN+3zDghRegTh0ID4d+/WD/fud9zp2DoUMhMhKqVoWHHoKUFG99gjwU3KRIZX1gAkDdunUJCQkhOzubY8eO+bo4PleWmkoDAwMJCQ4G1FwqIl6WmgodOsDMmfm//+qr8Pe/w+zZ8NNPUKkSDBgAl1stADO07dwJq1fDV1+ZYfCRR7xT/nwouEmR3D0ViGEYpKamkpqaimEYRR9QDAEBAY5geejQIbec05+5I7gZhkFmZiaZmZmlvk/q5yYiPnHLLfDXv8Kdd+Z9zzDg7bfhuefgjjugfXv48EP4/fecmrndu2HFCnjvPejRA665Bv7xD1i0yNzPBxTcpEjuDm5ZWVm8/vrrvP76625Z8squcePGgIIbuCe42Ww2Nm7cyMaNG0u15FXucii4iUhpXbhwwelR4i4Yhw5BYqLZPGoXFWUGtE2bzNebNpnNo5f7ZQPm/gEBZg2dDyi4SZF+++03AJo0aeLjkhROwS1HWWoqBQU3EXGf2NhYoqKiHI8ZM2aU7ESJieZ/a9d23l67ds57iYlQq5bz+0FBUL16zj5eplGlUqjk5GTOnTsH5ASjssoeLBXccgKSr9cptVNwExF3SUhIIDIy0vE6NDTUh6XxPgU3KZQ9BEVHR5fZOdzsVOOWIzU1FYBKlSr5uCQmBTcRcZfIyEin4FZiMTHmf0+eNEeV2p08CR075uxz6pTzcdnZ5khT+/FepqZSKZQ9BJX12jZQcMvt0qVLgIKbiEiBGjc2w9eaNTnbLlww+6717Gm+7tkTkpIgLi5nn2+/BZvN7AvnA6pxk0L5S/82yAluJ06cIC0trcz07/IFe41bRESEj0tiUnATEZ9ISYEDB3JeHzoE8fFmH7UGDWD8eHPUafPmZpB7/nlzzrdBg8z9W7eGm2+G0aPNKUOysmDcOPjjH839fEA1blIof6pxq169OlWqVAEq9goK2dnZZGRmAqpxE5EKbssW6NTJfABMnGg+f+EF8/Wf/wyPP27Oy9atmxn0VqyA3P2DFyyAVq3gxhvh1lvNKUH+/W/vf5bLVOMmhfJEjVtAQAAdOnRwPHcXi8VC48aN+fXXXzl06BCtWrVy27n9ib2ZNMBiKfWSVzGX+3CUZskryKn5s5dNRMQrrrvOnK+tIBYLvPii+ShI9eqwcKHbi1ZSCm5SKE/UuAUFBTHIXg3tZvbgZg+cFVHuZtLSBK6AgAC3hV97zZ+9bCIiUjJqKpUC2Ww2R3Dzhz5uoAEKUPYGJoCCm4iIuyi4SYESExPJyMggMDCQ2NhYt53XnUspXUnBzX0DEwzDwGq1YrVaS32f7MEtMyvLratliIhUNApuUiB7c2ODBg0ICnJfq3pWVhYzZsxgxowZbv8jrkl43TeHm81m44cffuCHH34o9ZJXISEhBAUGOpVPRERcp+AmBfKnEaV2qnErm02lFotFzaUiIm6g4CYF8qc53OwaNWoEQFJSEklJST4ti6+UtTnc7BTcRERKT8FNCuSPNW6VKlWi1uUFgStqrVtZrHGDnCCp4CYiUnKaDkQK5I81bmAGzVOnTnHo0CE62SddLG+mTs3/OWVvnVI71bj5kSt+pvK8FhGfUY2bFMgfa9xA/dzUVCoiUn6pxk3ylZGRwfHjxwH/q3Fr2rQpAAcPHvRxSbzkitqQstpUquAmIlJ6Cm6SryNHjmAYBpUqVaJmzZpuPXdAQABt2rRxPHe3Zs2aAbB//363n7uss1qtpGdkAKWvcbNYLERHRzuel5aCm4hI6Sm4Sb7s/dsaN27slj/auQUFBXHvvfe69Zy5NW/eHKiYwc0eigIsFsfC7iUVEBBA27Zt3VEsQMFNRMQd1MdN8mUPPfYQ5E/sNW5Hjx4l43LtU0VhbyYt7TqlnqDgJiJSeqpxk3z5c3CrVasWVapU4eLFi/z222+0bt3a10XymrI6MAGcg5thGGUuWEohChnFLCLepRo3yde+ffsAzwS3zMxMpk2bxrRp08jMzHT7+S0WS4VtLnXnwASr1cratWtZu3YtVqu11Oezl8lmGKSnp5f6fCIiFZFq3CRf/lzjBmZz6datWzlw4ICvi+IexazlKKtzuIHZtzE0JISMzExSU1NL3QdPRKQiUo2b5JGZmcnhw4cBaNGihW8LU0IVtcatLDeVgvq5iYiUloKb5HHo0CFsNhuVK1cmJibG18UpkYoa3MrqHG529nLZyykiIq5RcJM87P3bmjVr5rcdyO0jS8tNU2kxleWmUlCNm4hIaSm4SR7+3r8Ncsp+9OjRCtURXk2lIiLlm4Kb5GEPbv7avw0gOjqaKlWqYBiGYzLhisBfmkoV3ERESqZMBLeZM2fSqFEjwsLC6NGjBz///HOB+86ZM4c+ffpQrVo1qlWrRr9+/QrdX1znyalAwJyRv3nz5jRv3twjS16B85QgFam51J01bhaLhRo1alCjRg23NZkruImIlI7Pg9vixYuZOHEiU6ZMYevWrXTo0IEBAwZw6tSpfPdfu3YtQ4YM4bvvvmPTpk3ExsbSv39/x4LoUnqebioNCgri/vvv5/777ycoyHMz0lS0AQq51yl1R41bQEAA7dq1o127dm4L2ApuIiKl4/Pg9uabbzJ69GhGjRpFmzZtmD17NhEREXzwwQf57r9gwQL+9Kc/0bFjR1q1asV7772HzWZjzZo1Xi55+ZSWlkZCQgLg333coOINULA3k7pjnVJPUXATESkdnwa3zMxM4uLi6Nevn2NbQEAA/fr1Y9OmTcU6x6VLl8jKyqJ69er5vp+RkcGFCxccj4sXL7ql7OXVwYMHAahatSo1a9b0cWlKp6LVuNnDUHh4eJkdDazgJiJSOj4NbmfOnMFqtVK7dm2n7bVr1yYxMbFY53jmmWeoW7euU/jLbcaMGURFRTkebdq0KXW5y7Pc/ds89cc/MzOT6dOnM336dI8seWVXUYObuwYmWK1WfvjhB3744Qe3LHkFueZxS0vDZrO55ZwiIhWJz5tKS+OVV15h0aJFfPrpp4SFheW7z+TJk0lOTnY8du3a5eVS+hdvTQWSlZVFVlaWR69hbypNSEioEFOC2GuTq1Sp4rZzWq1Wt4U2uFwbePm5JuEVEXGdT4NbzZo1CQwM5OTJk07bT548WeSM/a+//jqvvPIKq1aton379gXuFxoaSmRkpOPhzj9q5VF5mMPNLjo6mqioKAzDqBC1bhcuXAAgMjLSxyUpWEBAgGPEq5pLRURc59PgFhISQpcuXZwGFtgHGvTs2bPA41599VVeeuklVqxYQdeuXb1R1ArD3lTqz3O42VksFkfT+O7du31cGs/zRI2bJ6ifm4hIyfm8qXTixInMmTOH+fPns3v3bsaMGUNqaiqjRo0CYPjw4UyePNmx/9/+9jeef/55PvjgAxo1akRiYiKJiYmkpKT46iOUK56ew83b7MGtIjSR+0ONG+QEN/0/KyLiOs9NolVMgwcP5vTp07zwwgskJibSsWNHVqxY4RiwcPToUac5pGbNmkVmZib33HOP03mmTJnC1KlTvVn0cufcuXOOZuvWrVv7uDTuoeBW9tjLZy+viIgUn8+DG8C4ceMYN25cvu+tXbvW6fXhw4c9X6AKaufOnQA0bNiQypUr+7g07mEPbvbPVp75S1OpgpuISMmVieAmZYM93Hh6yhSLxULDhg0dzz3J/ln27dtHVlYWwcHBHr2er1itVkefMXfWuFWtWtVt57JTcBMRKTkFN3GwB7e2bdt69DrBwcGMHDnSo9ewi42NpXLlyqSkpHDgwIFy0wR8pYsXL2IAgblGbZZWYGAgHTt2dMu5couKigIgOTnZ7ecWESnvFNzEwVvBzZssFgutW7dm8+bN7Nq1y3+Cm4v9NXM3k5bVVRPsVONWzlz5s6q+xiIe5fNRpVJ2lMfgBhVjgIK/DEyAnDKmXrpEdna2j0sjIuJfFNwEMJcfO3XqFOD5EaWZmZm89tprvPbaax5d8spOwa1krFYrGzZsYMOGDW5fPSEoMBBAaweLiLhITaUC5NS2NWrUyCsjSr253FFFCG6eGlHqiWXJLBYLUVFRnD13juTkZKpVq+b2a4gHqSlUxKdU4yZA+W0mhZzgtnfv3nLbNOdPTaWgfm4iIiWl4CZA+Q5uDRs2JDw8nIyMDA4dOuTr4niEgpuISMWg4CZA+Q5ugYGBtGrVCii/zaX+MvmunYKbiEjJKLgJkBNoymNwg/Ldz80wDL+rcdNcbiIiJaPgJpw+fZrTp0875jwrj8rz0leXLl3CarMBqnETEXGYOhUsFufH5dYXANLTYexYqFEDKleGu++Gy+t1l2UaVSqOMNO4cWO3zbpfGIvFQt26dR3PvaFdu3YA/Prrr165njfZm0krRUQQeHmaDXfxVBBUcCvHCht1qhGp4m1t28I33+S8DsoVeyZMgGXLYMkSiIqCcePgrrtgwwbvl9MFCm7i9f5twcHBjB492ivXsuvcuTNgNpWmpaURHh7u1et7kqeaSQMDA+nSpYtbz2l35SS8QUH6VSQiHhAUBDExebcnJ8P778PChXDDDea2uXOhdWv48Ue4+mrvltMFaioV4uPjgZxaqfKobt26REdHY7Va2b59u6+L41b+1r8NzEl4gy+HNdW6iYgrLly44PTIyMgoeOf9+6FuXWjSBIYOhaNHze1xcZCVBf365ezbqhU0aACbNnn2A5SSgpuwZcsWAI/VrpQFFovFUeu2detWH5fmsqlTcx6l4G8jSsG8H2ourYBy/8yr2VRKKDY2lqioKMdjxowZ+e/YowfMmwcrVsCsWXDoEPTpAxcvQmIihIRA1arOx9Subb5Xhql9ooJLT09nx44dgPeCW1ZWFjNnzgRg7NixBAcHe+W6nTt3ZuXKlWUnuLmJp2rcrFYrmzdvBqBbt25u7z8XGRnJ2XPnFNxExCUJCQlOv+9CQ0Pz3/GWW3Ket29vBrmGDeHjj8GPu8souFVw27dvJzs7mxo1atCgQQOvXNMwDMc0EIZheOWakNPP7ZdffvHaNb3Bk02l6enpbj+nnb28mhJERFwRGRlZst93VatCixZw4ADcdBNkZkJSknOt28mT+feJK0PUVFrBxcXFAdC1a1evjfD0lU6dOgHmyFJPrMHpK/7YVAo5c7mpxk1EvCIlBQ4ehDp1oEsXCA6GNWty3t+71+wD17On78pYDKpxq+Dswa0892+za9KkCVFRUSQnJ7Nr1y46dOjg6yLlKGF/H8MwOH/+PABVr+yrUcapj5uIeNTTT8Ntt5nNo7//DlOmQGAgDBliTv/x0EMwcSJUrw6RkfD442ZoK8MjSkHBrcKrSMHNYrHQqVMn1q5dy9atW70f3DzQGfvixYtkZWcTYLEouImI5HbsmBnSzp6F6Gi45hpzqo/oaPP9t96CgABz4t2MDBgwAN5917dlLgY1lVZgvhiY4GtlbmRpKZ09exaAatWquX3wgKepj5uIeNSiRWZNW0aGGeIWLYKmTXPeDwuDmTPh3DlITYWlS8t8/zZQcKvQtm/fTlZWllcHJviavZ9beRmgYA9uNWrU8HFJXFetWjUALqWleXQQhIhIeaLgVoHlbib15sAEi8VCdHQ00dHRXh8QYa9xi4+Px2q1evXannDu3DkAqlev7pHzV6pUiUqVKnnk3KGhoVSpXBmAM2fOeOQaIiLljfq4VWC+6t8WHBzMn/70J69e065ly5aEh4eTmprK/v37aZV7wWE/5Mkat8DAQLp16+b28+ZWs2ZNLqakcObMGerXr+/Ra4mIlAeqcavAKtLABLvAwEA6duwIwE8//eTbwriBPzeVghncIOdziIhI4RTcKqiMjIwKNzDBrnfv3gCsX7/exyUpHZvN5pgKxF+Dm73caioVESkeBbcKavPmzWRlZVGrVi0aNmzo1WtnZWXx7rvv8u677/pkItw+ffoA8MMPP3j92u6UnJyM1WYjKDDQI6sm2Je82rx5s8f6A9pr3BTcRESKR33cKqh169YBcO2113p9gIBhGJw+fdrx3NvsNW579+7l1KlT1KpVy+tlcAd782L16tU9dg9TU1M9cl47e3A7d+4cNpuNgAD9W1JEpDD6LVlB2YNb3759fVwS76tRowZt27YFYMOGDT4uTcn5e/82MJe9CgoMxGqzkZSU5OviiIiUeQpuFVBWVhYbN24EKmZwg/LRXFoegpvFYlE/NxERFyi4VUBbt24lNTWV6tWrO2qeKpryENw8PYebt2hkqYhI8amPWwVkbybt06dPhe1TZA9uv/zyCykpKVS+PBGsPykPNW6gkaWSS+71fD2wtq9IeVAx/2pXcBW5f5tdbGwsDRs2xGq1smnTJl8Xx2XZ2dmOPmH+Htw0slREpPgU3CoYq9XqmL/MV8HNYrEQFRVFVFSU10e05ubPzaXnz5/HAEJDQjy2JBVAWFgYYWFhHjs/qKlURMQVaiqtYOLj47lw4QJRUVF06NDBJ2UIDg5m/PjxPrl2bn369OG///2vXwa33P3bPBV+AwMDufrqqz1y7tzsNYYpqamkp6d7PChKGaLmUBGXqcatgvn+++8BuOaaawgMDPRxaXzLXuO4ceNGUlJSfFwa19ibFf29mRS02LyIiCsU3CqYNWvWAObEuxVdixYtaNKkCZmZmXzzzTe+Lo5Ljh8/DkBMTIyPS+Ieai4VESkeBbcKJCUlxRFQbrnlFp+VIysrizlz5jBnzhyfLHllZ7FYuO222wD48ssvfVaOkrAHt/r163vsGlarlbi4OOLi4jy25JWdPbjZV9QQEZH8KbhVIKtWrSIjI4MmTZpw1VVX+awchmHw+++/8/vvv/tkyavc7MFt2bJl2Gw2n5aluFJSUki+cAELUKdOHY9e6+LFi1y8eNGj14Ccz3Hs2DGPX0tExJ9pcEIF8vnnnwNwxx13+HQ0Z1nSp08fIiMjOXnyJFu2bKF79+6lO6EX5qGy17ZFR0cTGhrqkWt4W2xsLGB+Nq1ZKiJSMP12rCCys7P56quvABg0aJBvC1OGhISEMGDAAMB/mkvtwa1evXo+Lon71KxZk7DQULKyszl58qSviyMiUmapxq2CWL9+PefOnaNGjRr06tXL18UpU2677TaWLFnCl19+yUsvveS+E3uoxs3enFiegpvFYqF+/focOHiQhIQEjzcBix+48v8fTR0iAqjGrcL47LPPADOkBAUpr+d26623EhAQwLZt20hISPB1cQpl7x8I5Su4QU5zaVm/B1JGTJ2a8xCpQBTcKgDDMBzB7Y477vBtYcqg3LWQ9n6AZdXZs2dJz8ggOCiIWrVq+bo4bqXgJiJSNAW3CiA+Pp4jR44QHh5O//79fV0cACIiIoiIiPB1MRzuvvtuAObPn+/jkhTO3r+tTp06XplAOTg4mODgYI9fB8waRAuQlJzslZGsIiL+SMGtAnj//fcBs0mwLISlkJAQJk2axKRJkwgJCfF1cQB44IEHCA4OZsuWLcTHx/u6OAXy5sCEwMBAevfuTe/evb0SEkNDQx21iJoWREQkf+rsVM6lpqbyn//8B4BHH33Ux6Upu2rWrMmgQYNYsmQJ77//Pv/4xz/y39HH/WnK48CE3GJjYzl56hQJCQm0bt3a18URESlzVONWzi1atIgLFy7QtGlTbrzxRl8Xp0x7+OGHAfjvf/9LWlqaj0uTV1ZWlmOqjPIc3ED93ERECqLgVs7Nnj0bMGvbysqkpllZWcybN4958+b5dMmrK/Xr14+GDRuSlJTEp59+6uvi5HHgwAGsNhtVo6KoWrWqx69ntVqJj48nPj7e40te2dmD2++//052drZXriki4k/UVFqOxcXFsWXLFkJCQhg5cqSvi+NgGAZHjhxxPC8rAgICGDVqFFOnTuW9997j/vvv93WRnOzatQuANm3aeG3li6SkJK9cx65atWpUqVyZiykpHDp0iObNm3v1+lKGFdZNQXO+SQVSNqpgxCP+9a9/AeaIyejoaB+XxseKOefTqFGjmApc+913JD72mOfLVUzZ2dns27cPoFz3/bJYLLRp0waA7du3+7g0zv+wyL2W7ZXr2palf4CISPmmGrdy6ujRo3z44YcAPFaGAkhZ16BBA9q2bcuOnTv59ttvub+M/Mv94MGDZGRmElmlCvXr1/d1cTzqqquu4qeff2bPnj1kZWV5bTqSK509e5ZTp07RtGlTQkJCHF0NNm/eTHJyMtHR0TRt2pTKlStjsVgwDENrAIuIxym4lVPPP/88GRkZXH/99fTp08fXxfEr119/Pbt27WLf/v0cO3asTAQlezNp69aty304qF+/PlWjokhKTmbfvn20bdvW62U4evQon376KdWrV6d69erUrl0bMEPbjz/+SP/+/fnll184d+4c9evXp3nz5uX+vvgVNZ1KOaam0nJo27ZtjilAXn31Vf1BcVGNGjXo0KEDAGvWrPFxacxBAnv37gVwNCOWZxaLhXbt2gG+aS49fvw4//3vf+nUqRO33HKLI7QBZGRk0K9fP1q2bMktt9xCREQEhw8f5uDBg14vp4hUTKpxK4eeeeYZDMNg8ODBdO3a1dfFKfvy+dd43759+fXXXzl0+DCHDh2icePG3i/XZYcOHSI9I4PKlSo5Rl2Wd1dddRU/rF/P/v37SU9PJywszCvXTUtL49tvv+Waa67h2muvzfN+ZGQkGzdupG7dukRFRdG2bVu2b9/OsWPHaNq0qVfKKG6m2jnxM6pxK2dWrlzJypUrCQ4O5uWXX/Z1cQrkzaWU8sg9UKGAX9JVq1Z1hN7ly5f7dNqSbdu2AWYzqbendAkMDPTKqglXql27NrWio7HabOzevdtr17VaraSlpTmFsLS0NI4fP85vv/1GgwYNaNeuHT/99BMXL16kcuXKtG7dmp07d2ruORHxCtW4lSMnT550TPvxpz/9qczWAISEhPCXv/zF18UoUt++fdm1axenz5xh1apVDBw40OtlOHXqFDt27ACgU6dOXr12YGCgT/tHXnXVVXz73Xds3ryZjh07eqXJ//z58yQmJjr+UbFt2zZ2797N3r17CQoKomnTpnTo0IG0tDQ2bNhAz549qVq1KnXq1CEjI8Pj5RMRUY1bOWG1WnnggQdITEykbdu2TJ8+3ddF8nsREREMGjQIgM1btjj6mXnTmjVrMIA2rVtTt25dr1/flzp37kxIcDC/nzjhtb5u1atXp27duixevJh58+axatUqwsLCGDJkCNdddx2HDx8mIiKCq666ikqVKvHhhx+ydOlSEhMTadKkiVfKKCVQjFp2EX+h4FZOvPzyy3zzzTdERESwZMmSMrGYfHnQtGlTevXsCcDnn3/O+fPnvXbthIQE9u7bR4DFwg033OC165YVlStXdvQz++abb8jMzPT4NStVqkSXLl2IiYnBMAzuuecebrrpJlq0aEHv3r0xDIPTp09To0YN+vTpw1133UW3bt0cK5NcOb+biIi7KbiVA3//+9+ZMmUKALNmzSrzE7RmZ2ezcOFCFi5c6BfLGt1www3UiYnhUloac+fO5dy5cx6/pmEYfPPNNwB07NiRmjVrevyaV7LZbGzfvp3t27f7LJBcffXVVI2K4sLFi2zcuNGj17JPotupUyfuvvtuRo4cSePGjalUqRKGYXDq1Cmio6OpU6eO45h69eoRGxvrCG1lZVk5ESm/1MfNjxmGwV//+ldeeOEFAJ5++mmGDx/u41IVzWazsX//fsfzsi4oKIihQ4cyb948zpw9y7x58xgxYgQ1atTw2DV//PFHjhw9SlBgINddd53HrlMYwzA4e/as47kvBAUF0b9/fz5esoQNGzbQrl07j33vuSfRDQgIcPrMFouFuLg4AgMDC7y+Qpsf0fJZ4sf0m8ZPnT9/nlGjRjlC29SpU3n11Vd9XKryq3LlyowcOZLomjW5cPEic+bMYdu2bR4JNNu2bWPlqlWAWdsXGRnp9mv4k9atW9OoYUOysrOZP3++R2s8cw+AsD8/duwYX3zxBTt27GDQoEGEhYVpiSsR8ZkyEdxmzpxJo0aNCAsLo0ePHvz888+F7r9kyRJatWpFWFgY7dq1Y/ny5V4qqe/ZbDaWLFlCmzZtmD9/PhaLhbfeeospU6Zool0Ps4e3+vXqkZ6RwaeffcbHH3/s1iCxa9cuPv/8cwB6Xn01PS/3r6vILBYL99xzjyM0z58/v+R9DW02OHwYtm83/1tEje/69etZvXo1Z86cYfTo0VSrVg2bzab/10T8ycyZ0KgRhIVBjx5QRMYo63zeVLp48WImTpzI7Nmz6dGjB2+//TYDBgxg79691KpVK8/+GzduZMiQIcyYMYM//OEPLFy4kEGDBrF161auuuoqH3wC70hOTua///0v//jHPxyjG1u1asV7771H7969fVw6N3FnE4WHmjcqVarEgw8+yPr161m3bh279+xhz549tG7dmu7du9OgQYMSNZklJSWxcuVKdu/ZA0CH9u3p37+/AsJllStXZsSIEY7m6n//+99ce+21dOvWjaCgYv4a270bvv4aLl7M2ValCtxyCxTQL7R9+/ZUq1aNxo0bExERoX5sFVHu3yVqNvU/ixfDxIkwe7YZ2t5+GwYMgL17IZ+M4Q8sho/r/Hv06EG3bt345z//CZg1SrGxsTz++OM8++yzefYfPHgwqampfPXVV45tV199NR07dmT27NlFXu/YsWPExsaSkJBQJtagzI9hGBw/fpxff/2VuLg4Vq1axaZNm7BarYA5e/v48eOZPHmy12aUd6fMzExmzJgBwOTJkwkJCTHfKCq4lbF+KYmJiaxZs4b9Bw44toWHhdG0aVPq169P7dq1qVmzJhEREXkmsc3MzCQ5OZkjR46wf/9+Dh48SLbVSoDFQo8ePejXr59PJr7NzWq18sMPPwDQp08fn5cH4OLFiyxcuJATiYkAVKtalQ4dOtC8eXPq1q1bcNDdvRs+/rjgE993X4HhzU6LyIvLFPTc6sKFC0RFRZGcnFz8LiQ9ekC3bnA5Y2CzQWwsPP445JMx/IFPa9wyMzOJi4tj8uTJjm0BAQH069ePTZs25XvMpk2bmDhxotO2AQMG8Nlnn3myqEU6e/Ys3377LYZhOD1sNhs2m43s7GysVitZWVlkZmaSkZHBpUuXSEtLIykpiaSkJM6cOcPx48dJSEjg0qVLea7Rpk0b/vSnPzF8+HCqVKnig09ZhvngF2RMTAxDhw7l1KlT/Pjjj+zevZu09HR27NzJjp07nfYNCw111AxlZ2eTns9krY0aNuTWW2/Nt6ZZTFWqVGH06NHEx8fz7bffcj4pibXr1rF23TpCgoOpXr06NWrUoFKlSoSHhxMaGkpwYCAd1q4lGMgvdhlA9pdfcsBmAzfUpgUHB9OsWbNSn0fEwVP/MC3vAzEyMyEuDnJlDAICoF8/KCBj+AOfBrczZ85gtVqdFnEGc7mbPZebjK6UmJiY7/6Jl/8FfqWMjAynGc2Tk5MBOHHiRGmKnkdcXBz33Xef285nsVho2rQpbdq04eqrr6Zv3740aNAAMD+D/XP4o6ysLEf5jx8/7pilPvTCBaf9Mo4dc3p95ftlhSUsjJ7XXUePa6/lxIkTHDlyhNOnT3P27FmSLn/OjIwMuCKshQQHU6NGDZo0aULjxo2Jjo7GYrFwuox8TqvVSnJ6OgCnL1woEzVudrHNmjGkQQP27t3LoUOHOHLkCBezsrh48iRHTp502rc+0KioE6al8e0nn3CsqP2KISoykoceesgNZxK/l6uSIeOKCofQN990y3nzO3dh18m9b1G/c11R2HXc5eLlbg5JSUnO1w4NJTQ0NO8BZ86A1QpXZAZq14YCMoZfMHzo+PHjBmBs3LjRafukSZOM7t2753tMcHCwsXDhQqdtM2fONGrVqpXv/lOmTDEw/1Gthx566KGHHnqUs8eUKVMKChmGAYZxRcYwJk0yjAIyhj/waY1bzZo1CQwM5OQV/0I+efIkMTEx+R4TExPj0v6TJ092alrNzs5m9+7djkkzS+PixYu0adOGXbt2VeimS30PJn0POfRdmPQ9mPQ9mPQ95CjJd2EYBufOnaNBgwZOLQD51rYB1KwJgYFwRWbg5EkoIDP4A58Gt5CQELp06cKaNWsca0LabDbWrFnDuHHj8j2mZ8+erFmzhvHjxzu2rV69usBpE/KrQnXXKMwLl6uZ69WrV6Hn2tL3YNL3kEPfhUnfg0nfg0nfQ46SfhexsbHFv0hICHTpAmvWwOWMgc1mvi4gY/gDn08HMnHiREaMGEHXrl3p3r07b7/9NqmpqYwaNQqA4cOHU69ePccoxCeffJK+ffvyxhtvMHDgQBYtWsSWLVv497//7cuPISIiImXNxIkwYgR07Qrdu5vTgaSmwuWM4Y98HtwGDx7M6dOneeGFF0hMTKRjx46sWLHCMQDh6NGjTk2avXr1YuHChTz33HP85S9/oXnz5nz22Wfleg43ERERKYHBg+H0aXjhBUhMhI4dYcWKvAMW/IjPgxvAuHHjCmwaXbt2bZ5t9957L/fee6+HS1W00NBQpkyZUnD7egWh78Gk7yGHvguTvgeTvgeTvoccXv0uxo3z66bRK/l8Al4RERERKR6t3SIiIiLiJxTcRERERPyEgpuIiIiIn1BwExEREfETCm6FePnll+nVqxcRERFUrVo1332OHj3KwIEDiYiIoFatWkyaNIns7OxCz3vu3DmGDh1KZGQkVatW5aGHHiIlJcUDn8Az1q5di8ViyfexefPmAo+77rrr8uz/2GOPebHk7teoUaM8n+mVV14p9Jj09HTGjh1LjRo1qFy5MnfffXee1UD8yeHDh3nooYdo3Lgx4eHhNG3alClTppCZmVnoceXl52HmzJk0atSIsLAwevTowc8//1zo/kuWLKFVq1aEhYXRrl07li9f7qWSesaMGTPo1q0bVapUoVatWgwaNIi9e/cWesy8efPy3PuwsDAvldhzpk6dmudztWrVqtBjytvPA+T/e9FisTB27Nh89y+vPw+eouBWiMzMTO69917GjBmT7/tWq5WBAweSmZnJxo0bmT9/PvPmzeOFF14o9LxDhw5l586drF69mq+++orvv/+eRx55xBMfwSN69erFiRMnnB4PP/wwjRs3pmvXroUeO3r0aKfjXn31VS+V2nNefPFFp8/0+OOPF7r/hAkT+PLLL1myZAnr1q3j999/56677vJSad1vz5492Gw2/vWvf7Fz507eeustZs+ezV/+8pcij/X3n4fFixczceJEpkyZwtatW+nQoQMDBgzg1KlT+e6/ceNGhgwZwkMPPcQvv/zCoEGDGDRoEDt27PByyd1n3bp1jB07lh9//JHVq1eTlZVF//79SU1NLfS4yMhIp3t/5MgRL5XYs9q2bev0udavX1/gvuXx5wFg8+bNTt/B6tWrAQqdxqu8/jx4hI/XSvULc+fONaKiovJsX758uREQEGAkJiY6ts2aNcuIjIw0MjIy8j3Xrl27DMDYvHmzY9vXX39tWCwW4/jx424vuzdkZmYa0dHRxosvvljofn379jWefPJJ7xTKSxo2bGi89dZbxd4/KSnJCA4ONpYsWeLYtnv3bgMwNm3a5IES+sarr75qNG7cuNB9ysPPQ/fu3Y2xY8c6XlutVqNu3brGjBkz8t3/vvvuMwYOHOi0rUePHsajjz7q0XJ606lTpwzAWLduXYH7FPQ71d9NmTLF6NChQ7H3rwg/D4ZhGE8++aTRtGlTw2az5ft+ef158BTVuJXCpk2baNeunWOVB4ABAwZw4cIFdu7cWeAxVatWdaqZ6tevHwEBAfz0008eL7MnfPHFF5w9e9axTFlhFixYQM2aNbnqqquYPHkyly5d8kIJPeuVV16hRo0adOrUiddee63QpvK4uDiysrLo16+fY1urVq1o0KABmzZt8kZxvSI5OZnq1asXuZ8//zxkZmYSFxfndC8DAgLo169fgfdy06ZNTvuD+TujvN17oMj7n5KSQsOGDYmNjeWOO+4o8Hemv9m/fz9169alSZMmDB06lKNHjxa4b0X4ecjMzOS///0vDz74IBaLpcD9yuvPgyeUiZUT/FViYqJTaAMcrxMTEws8platWk7bgoKCqF69eoHHlHXvv/8+AwYMoH79+oXud//999OwYUPq1q3Lr7/+yjPPPMPevXtZunSpl0rqfk888QSdO3emevXqbNy4kcmTJ3PixAnefPPNfPdPTEwkJCQkT5/J2rVr++39v9KBAwf4xz/+weuvv17ofv7+83DmzBmsVmu+vwP27NmT7zEF/c4oL/feZrMxfvx4evfuXegyhC1btuSDDz6gffv2JCcn8/rrr9OrVy927txZ5O+RsqxHjx7MmzePli1bcuLECaZNm0afPn3YsWMHVapUybN/ef95APjss89ISkpi5MiRBe5TXn8ePMbXVX7e9swzzxhAoY/du3c7HVNQNe7o0aON/v37O21LTU01AGP58uX5Xv/ll182WrRokWd7dHS08e6775b8g7lBSb6bhIQEIyAgwPjkk09cvt6aNWsMwDhw4IC7PoJblOR7sHv//feNoKAgIz09Pd/3FyxYYISEhOTZ3q1bN+PPf/6zWz9HaZXkezh27JjRtGlT46GHHnL5emX156Egx48fNwBj48aNTtsnTZpkdO/ePd9jgoODjYULFzptmzlzplGrVi2PldObHnvsMaNhw4ZGQkKCS8dlZmYaTZs2NZ577jkPlcw3zp8/b0RGRhrvvfdevu+X958HwzCM/v37G3/4wx9cOqa8/jy4S4WrcXvqqacKTf4ATZo0Kda5YmJi8owgs48OjImJKfCYKzsuZ2dnc+7cuQKP8ZaSfDdz586lRo0a3H777S5fr0ePHoBZQ9O0aVOXj/eU0vyM9OjRg+zsbA4fPkzLli3zvB8TE0NmZiZJSUlOtW4nT570+f2/kqvfw++//871119Pr169+Pe//+3y9crqz0NBatasSWBgYJ4RwYXdy5iYGJf29yfjxo1zDLZytZYkODiYTp06ceDAAQ+VzjeqVq1KixYtCvxc5fnnAeDIkSN88803Lteil9efB3epcMEtOjqa6Ohot5yrZ8+evPzyy5w6dcrR/Ll69WoiIyNp06ZNgcckJSURFxdHly5dAPj222+x2WyOP1y+4up3YxgGc+fOZfjw4QQHB7t8vfj4eADq1Knj8rGeVJqfkfj4eAICAvI0h9t16dKF4OBg1qxZw9133w3A3r17OXr0KD179ixxmT3Ble/h+PHjXH/99XTp0oW5c+cSEOB699my+vNQkJCQELp06cKaNWsYNGgQYDYVrlmzhnEFLGjds2dP1qxZw/jx4x3bVq9eXebuvSsMw+Dxxx/n008/Ze3atTRu3Njlc1itVrZv386tt97qgRL6TkpKCgcPHmTYsGH5vl8efx5ymzt3LrVq1WLgwIEuHVdefx7cxtdVfmXZkSNHjF9++cWYNm2aUblyZeOXX34xfvnlF+PixYuGYRhGdna2cdVVVxn9+/c34uPjjRUrVhjR0dHG5MmTHef46aefjJYtWxrHjh1zbLv55puNTp06GT/99JOxfv16o3nz5saQIUO8/vlK65tvvimw2fDYsWNGy5YtjZ9++skwDMM4cOCA8eKLLxpbtmwxDh06ZHz++edGkyZNjGuvvdbbxXabjRs3Gm+99ZYRHx9vHDx40Pjvf/9rREdHG8OHD3fsc+X3YBhmc1KDBg2Mb7/91tiyZYvRs2dPo2fPnr74CG5x7Ngxo1mzZsaNN95oHDt2zDhx4oTjkXuf8vjzsGjRIiM0NNSYN2+esWvXLuORRx4xqlat6hhpPmzYMOPZZ5917L9hwwYjKCjIeP31143du3cbU6ZMMYKDg43t27f76iOU2pgxY4yoqChj7dq1Tvf+0qVLjn2u/B6mTZtmrFy50jh48KARFxdn/PGPfzTCwsKMnTt3+uIjuM1TTz1lrF271jh06JCxYcMGo1+/fkbNmjWNU6dOGYZRMX4e7KxWq9GgQQPjmWeeyfNeRfl58BQFt0KMGDEi33493333nWOfw4cPG7fccosRHh5u1KxZ03jqqaeMrKwsx/vfffedARiHDh1ybDt79qwxZMgQo3LlykZkZKQxatQoRxj0J0OGDDF69eqV73uHDh1y+q6OHj1qXHvttUb16tWN0NBQo1mzZsakSZOM5ORkL5bYveLi4owePXoYUVFRRlhYmNG6dWtj+vTpTv3brvweDMMw0tLSjD/96U9GtWrVjIiICOPOO+90Cjn+Zu7cuQX2gbMrzz8P//jHP4wGDRoYISEhRvfu3Y0ff/zR8V7fvn2NESNGOO3/8ccfGy1atDBCQkKMtm3bGsuWLfNyid2roHs/d+5cxz5Xfg/jx493fGe1a9c2br31VmPr1q3eL7ybDR482KhTp44REhJi1KtXzxg8eLBTn82K8PNgt3LlSgMw9u7dm+e9ivLz4CkWwzAML1bwiYiIiEgJaR43ERERET+h4CYiIiLiJxTcRERERPyEgpuIiIiIn1BwExEREfETCm4iIiIifkLBTURERMRPKLiJSLk2b948p3VhC2KxWPjss888Xh4RkdJQcBMRt7BarfTq1Yu77rrLaXtycjKxsbH83//9X4HHXnfddVgsFiwWC2FhYbRp04Z3333XLeUaPHgw+/btc7yeOnUqHTt2zLPfiRMnuOWWW9xyTRERT1FwExG3CAwMZN68eaxYsYIFCxY4tj/++ONUr16dKVOmFHr86NGjOXHiBLt27eK+++5j7NixfPTRR6UuV3h4OLVq1Spyv5iYGEJDQ0t9PRERT1JwExG3adGiBa+88gqPP/44J06c4PPPP2fRokV8+OGHhISEFHpsREQEMTExNGnShKlTp9K8eXO++OILAI4ePcodd9xB5cqViYyM5L777uPkyZOOY7dt28b1119PlSpViIyMpEuXLmzZsgVwbiqdN28e06ZNY9u2bY4avnnz5gF5m0q3b9/ODTfcQHh4ODVq1OCRRx4hJSXF8f7IkSMZNGgQr7/+OnXq1KFGjRqMHTuWrKwsN3yTIiL5C/J1AUSkfHn88cf59NNPGTZsGNu3b+eFF16gQ4cOLp8nPDyczMxMbDabI7StW7eO7Oxsxo4dy+DBg1m7di0AQ4cOpVOnTsyaNYvAwEDi4+MJDg7Oc87BgwezY8cOVqxYwTfffANAVFRUnv1SU1MZMGAAPXv2ZPPmzZw6dYqHH36YcePGOYIewHfffUedOnX47rvvOHDgAIMHD6Zjx46MHj3a5c8rIlIcCm4i4lYWi4VZs2bRunVr2rVrx7PPPuvS8VarlY8++ohff/2VRx55hDVr1rB9+3YOHTpEbGwsAB9++CFt27Zl8+bNdOvWjaNHjzJp0iRatWoFQPPmzfM9d3h4OJUrVyYoKIiYmJgCy7Bw4ULS09P58MMPqVSpEgD//Oc/ue222/jb3/5G7dq1AahWrRr//Oc/CQwMpFWrVgwcOJA1a9YouImIx6ipVETc7oMPPiAiIoJ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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "validation_samples = gflownet.sample_terminating_states(env, 10000)\n", + "render(env, validation_samples=validation_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0I6Je3gLjRIC" + }, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rleItlaVmRAh" + }, + "outputs": [], + "source": [ + "gflownet.logZ = torch.nn.init.constant_(gflownet.logZ, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RgG1SPiOmqHD" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tutorials/notebooks/intro_gfn_continuous_line_simple.ipynb b/tutorials/notebooks/intro_gfn_continuous_line_simple.ipynb index 60815aba..29a61420 100644 --- a/tutorials/notebooks/intro_gfn_continuous_line_simple.ipynb +++ b/tutorials/notebooks/intro_gfn_continuous_line_simple.ipynb @@ -859,7 +859,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/tutorials/notebooks/intro_gfn_smiley.ipynb b/tutorials/notebooks/intro_gfn_smiley.ipynb index 76c15355..3ab820b2 100644 --- a/tutorials/notebooks/intro_gfn_smiley.ipynb +++ b/tutorials/notebooks/intro_gfn_smiley.ipynb @@ -1932,7 +1932,7 @@ "source": [ "# Trajectory Balance with `torchgfn`\n", "\n", - "Similarly, we can train a gflownet using Trajectory Balance using the `TBGFlowNet` class. Unlike before, we separately parameterize the forward and backward policies are two different `estimators`, which are passed to the `TBGFlowNet`. In this example we don't use a replay buffer, so we set `on_policy=True`.\n", + "Similarly, we can train a gflownet using Trajectory Balance using the `TBGFlowNet` class. Unlike before, we separately parameterize the forward and backward policies are two different `estimators`, which are passed to the `TBGFlowNet`. In this example we don't use a replay buffer, so we set `off_policy=False`.\n", "\n", "One common trick with trajectory balance is to learn the `logZ` parameter with a higher learning rate than the rest of the network." ] @@ -1987,7 +1987,7 @@ "gflownet = TBGFlowNet(\n", " pf=pf_estimator,\n", " pb=pb_estimator,\n", - " on_policy=True, # No replay buffer.\n", + " off_policy=False, # No replay buffer.\n", ")\n", "\n", "# Policy parameters recieve one LR, and LogZ gets a dedicated, typically higher LR.\n",