Update LFNA version 1.0
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D-X-Y
@@ -2,6 +2,7 @@
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.05 #
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#####################################################
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from .synthetic_utils import TimeStamp
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from .synthetic_env import EnvSampler
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from .synthetic_env import SyntheticDEnv
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from .math_core import LinearFunc
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from .math_core import DynamicLinearFunc
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@@ -2,7 +2,7 @@
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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import math
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import abc
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import random
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import numpy as np
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from typing import List, Optional, Dict
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import torch
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@@ -11,6 +11,28 @@ import torch.utils.data as data
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from .synthetic_utils import TimeStamp
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def is_list_tuple(x):
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return isinstance(x, (tuple, list))
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def zip_sequence(sequence):
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def _combine(*alist):
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if is_list_tuple(alist[0]):
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return [_combine(*xlist) for xlist in zip(*alist)]
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else:
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return torch.cat(alist, dim=0)
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def unsqueeze(a):
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if is_list_tuple(a):
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return [unsqueeze(x) for x in a]
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else:
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return a.unsqueeze(dim=0)
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with torch.no_grad():
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sequence = [unsqueeze(a) for a in sequence]
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return _combine(*sequence)
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class SyntheticDEnv(data.Dataset):
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"""The synethtic dynamic environment."""
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@@ -33,7 +55,7 @@ class SyntheticDEnv(data.Dataset):
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self._num_per_task = num_per_task
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if timestamp_config is None:
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timestamp_config = dict(mode=mode)
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else:
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elif "mode" not in timestamp_config:
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timestamp_config["mode"] = mode
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self._timestamp_generator = TimeStamp(**timestamp_config)
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@@ -42,6 +64,7 @@ class SyntheticDEnv(data.Dataset):
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self._cov_functors = cov_functors
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self._oracle_map = None
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self._seq_length = None
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@property
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def min_timestamp(self):
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@@ -55,9 +78,18 @@ class SyntheticDEnv(data.Dataset):
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def timestamp_interval(self):
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return self._timestamp_generator.interval
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def reset_max_seq_length(self, seq_length):
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self._seq_length = seq_length
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def get_timestamp(self, index):
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index, timestamp = self._timestamp_generator[index]
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return timestamp
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if index is None:
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timestamps = []
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for index in range(len(self._timestamp_generator)):
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timestamps.append(self._timestamp_generator[index][1])
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return tuple(timestamps)
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else:
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index, timestamp = self._timestamp_generator[index]
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return timestamp
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def set_oracle_map(self, functor):
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self._oracle_map = functor
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@@ -75,7 +107,14 @@ class SyntheticDEnv(data.Dataset):
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def __getitem__(self, index):
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assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
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index, timestamp = self._timestamp_generator[index]
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return self.__call__(timestamp)
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if self._seq_length is None:
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return self.__call__(timestamp)
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else:
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timestamps = [
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timestamp + i * self.timestamp_interval for i in range(self._seq_length)
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]
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xdata = [self.__call__(timestamp) for timestamp in timestamps]
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return zip_sequence(xdata)
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def __call__(self, timestamp):
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mean_list = [functor(timestamp) for functor in self._mean_functors]
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@@ -88,10 +127,13 @@ class SyntheticDEnv(data.Dataset):
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mean_list, cov_matrix, size=self._num_per_task
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)
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if self._oracle_map is None:
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return timestamp, torch.Tensor(dataset)
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return torch.Tensor([timestamp]), torch.Tensor(dataset)
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else:
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targets = self._oracle_map.noise_call(dataset, timestamp)
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return timestamp, (torch.Tensor(dataset), torch.Tensor(targets))
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return torch.Tensor([timestamp]), (
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torch.Tensor(dataset),
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torch.Tensor(targets),
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)
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def __len__(self):
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return len(self._timestamp_generator)
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@@ -104,3 +146,20 @@ class SyntheticDEnv(data.Dataset):
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ndim=self._ndim,
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num_per_task=self._num_per_task,
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)
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class EnvSampler:
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def __init__(self, env, batch, enlarge):
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indexes = list(range(len(env)))
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self._indexes = indexes * enlarge
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self._batch = batch
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self._iterations = len(self._indexes) // self._batch
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def __iter__(self):
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random.shuffle(self._indexes)
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for it in range(self._iterations):
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indexes = self._indexes[it * self._batch : (it + 1) * self._batch]
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yield indexes
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def __len__(self):
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return self._iterations
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@@ -30,6 +30,7 @@ class UnifiedSplit:
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self._indexes = all_indexes[num_of_train + num_of_valid :]
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else:
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raise ValueError("Unkonwn mode of {:}".format(mode))
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self._all_indexes = all_indexes
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self._mode = mode
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@property
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@@ -1,120 +0,0 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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# DISABLED / NOT-FINISHED
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#####################################################
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import math
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from typing import Optional, Callable
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import spaces
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from .super_container import SuperSequential
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from .super_linear import SuperLinear
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class SuperActor(SuperModule):
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"""A Actor in RL."""
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def _distribution(self, obs):
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raise NotImplementedError
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def _log_prob_from_distribution(self, pi, act):
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raise NotImplementedError
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def forward_candidate(self, **kwargs):
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return self.forward_raw(**kwargs)
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def forward_raw(self, obs, act=None):
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# Produce action distributions for given observations, and
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# optionally compute the log likelihood of given actions under
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# those distributions.
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pi = self._distribution(obs)
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logp_a = None
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if act is not None:
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logp_a = self._log_prob_from_distribution(pi, act)
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return pi, logp_a
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class SuperLfnaMetaMLP(SuperModule):
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def __init__(self, obs_dim, hidden_sizes, act_cls):
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super(SuperLfnaMetaMLP).__init__()
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self.delta_net = SuperSequential(
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SuperLinear(obs_dim, hidden_sizes[0]),
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act_cls(),
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SuperLinear(hidden_sizes[0], hidden_sizes[1]),
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act_cls(),
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SuperLinear(hidden_sizes[1], 1),
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)
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class SuperLfnaMetaMLP(SuperModule):
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def __init__(self, obs_dim, act_dim, hidden_sizes, act_cls):
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super(SuperLfnaMetaMLP).__init__()
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log_std = -0.5 * np.ones(act_dim, dtype=np.float32)
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self.log_std = torch.nn.Parameter(torch.as_tensor(log_std))
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self.mu_net = SuperSequential(
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SuperLinear(obs_dim, hidden_sizes[0]),
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act_cls(),
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SuperLinear(hidden_sizes[0], hidden_sizes[1]),
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act_cls(),
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SuperLinear(hidden_sizes[1], act_dim),
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)
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def _distribution(self, obs):
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mu = self.mu_net(obs)
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std = torch.exp(self.log_std)
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return Normal(mu, std)
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def _log_prob_from_distribution(self, pi, act):
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return pi.log_prob(act).sum(axis=-1)
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def forward_candidate(self, **kwargs):
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return self.forward_raw(**kwargs)
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def forward_raw(self, obs, act=None):
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# Produce action distributions for given observations, and
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# optionally compute the log likelihood of given actions under
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# those distributions.
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pi = self._distribution(obs)
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logp_a = None
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if act is not None:
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logp_a = self._log_prob_from_distribution(pi, act)
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return pi, logp_a
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class SuperMLPGaussianActor(SuperModule):
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def __init__(self, obs_dim, act_dim, hidden_sizes, act_cls):
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super(SuperMLPGaussianActor).__init__()
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log_std = -0.5 * np.ones(act_dim, dtype=np.float32)
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self.log_std = torch.nn.Parameter(torch.as_tensor(log_std))
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self.mu_net = SuperSequential(
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SuperLinear(obs_dim, hidden_sizes[0]),
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act_cls(),
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SuperLinear(hidden_sizes[0], hidden_sizes[1]),
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act_cls(),
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SuperLinear(hidden_sizes[1], act_dim),
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)
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def _distribution(self, obs):
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mu = self.mu_net(obs)
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std = torch.exp(self.log_std)
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return Normal(mu, std)
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def _log_prob_from_distribution(self, pi, act):
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return pi.log_prob(act).sum(axis=-1)
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def forward_candidate(self, **kwargs):
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return self.forward_raw(**kwargs)
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def forward_raw(self, obs, act=None):
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# Produce action distributions for given observations, and
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# optionally compute the log likelihood of given actions under
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# those distributions.
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pi = self._distribution(obs)
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logp_a = None
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if act is not None:
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logp_a = self._log_prob_from_distribution(pi, act)
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return pi, logp_a
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@@ -42,6 +42,7 @@ class SuperTransformerEncoderLayer(SuperModule):
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qkv_bias: BoolSpaceType = False,
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mlp_hidden_multiplier: IntSpaceType = 4,
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drop: Optional[float] = None,
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norm_affine: bool = True,
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act_layer: Callable[[], nn.Module] = nn.GELU,
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order: LayerOrder = LayerOrder.PreNorm,
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):
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@@ -62,19 +63,19 @@ class SuperTransformerEncoderLayer(SuperModule):
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drop=drop,
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)
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if order is LayerOrder.PreNorm:
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self.norm1 = SuperLayerNorm1D(d_model)
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self.norm1 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
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self.mha = mha
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self.drop1 = nn.Dropout(drop or 0.0)
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self.norm2 = SuperLayerNorm1D(d_model)
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self.norm2 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
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self.mlp = mlp
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self.drop2 = nn.Dropout(drop or 0.0)
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elif order is LayerOrder.PostNorm:
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self.mha = mha
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self.drop1 = nn.Dropout(drop or 0.0)
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self.norm1 = SuperLayerNorm1D(d_model)
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self.norm1 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
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self.mlp = mlp
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self.drop2 = nn.Dropout(drop or 0.0)
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self.norm2 = SuperLayerNorm1D(d_model)
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self.norm2 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
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else:
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raise ValueError("Unknown order: {:}".format(order))
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self._order = order
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@@ -60,4 +60,7 @@ def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
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>>> w = torch.empty(3, 5)
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>>> nn.init.trunc_normal_(w)
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"""
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return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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if isinstance(tensor, list):
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return [_no_grad_trunc_normal_(x, mean, std, a, b) for x in tensor]
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else:
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return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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