Update LFNA version 1.0

exps/LFNA/lfna.py

@@ -1,7 +1,7 @@
 #####################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
 #####################################################
-# python exps/LFNA/lfna.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 50000
+# python exps/LFNA/lfna.py --env_version v1
 #####################################################
 import sys, time, copy, torch, random, argparse
 from tqdm import tqdm
@@ -19,56 +19,82 @@ from utils import split_str2indexes
 
 from procedures.advanced_main import basic_train_fn, basic_eval_fn
 from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
-from datasets.synthetic_core import get_synthetic_env
+from datasets.synthetic_core import get_synthetic_env, EnvSampler
 from models.xcore import get_model
 from xlayers import super_core, trunc_normal_
 
-
 from lfna_utils import lfna_setup, train_model, TimeData
+from lfna_meta_model import LFNA_Meta
 
-from lfna_models_v2 import HyperNet
+
+def epoch_train(loader, meta_model, base_model, optimizer, criterion, device, logger):
+    base_model.train()
+    meta_model.train()
+    loss_meter = AverageMeter()
+    for ibatch, batch_data in enumerate(loader):
+        timestamps, (batch_seq_inputs, batch_seq_targets) = batch_data
+        timestamps = timestamps.squeeze(dim=-1).to(device)
+        batch_seq_inputs = batch_seq_inputs.to(device)
+        batch_seq_targets = batch_seq_targets.to(device)
+
+        optimizer.zero_grad()
+
+        batch_seq_containers = meta_model(timestamps)
+        losses = []
+        for seq_containers, seq_inputs, seq_targets in zip(
+            batch_seq_containers, batch_seq_inputs, batch_seq_targets
+        ):
+            for container, inputs, targets in zip(
+                seq_containers, seq_inputs, seq_targets
+            ):
+                predictions = base_model.forward_with_container(inputs, container)
+                loss = criterion(predictions, targets)
+                losses.append(loss)
+        final_loss = torch.stack(losses).mean()
+        final_loss.backward()
+        optimizer.step()
+        loss_meter.update(final_loss.item())
+    return loss_meter
 
 
 def main(args):
     logger, env_info, model_kwargs = lfna_setup(args)
-    dynamic_env = env_info["dynamic_env"]
+    dynamic_env = get_synthetic_env(mode="train", version=args.env_version)
-    model = get_model(**model_kwargs)
+    base_model = get_model(**model_kwargs)
-    model = model.to(args.device)
+    base_model = base_model.to(args.device)
     criterion = torch.nn.MSELoss()
 
-    logger.log("There are {:} weights.".format(model.get_w_container().numel()))
+    shape_container = base_model.get_w_container().to_shape_container()
-    # meta_train_range = (dynamic_env.min_timestamp, (dynamic_env.min_timestamp + dynamic_env.max_timestamp) / 2)
-    # meta_train_interval = dynamic_env.timestamp_interval
-
-    shape_container = model.get_w_container().to_shape_container()
 
     # pre-train the hypernetwork
-    timestamps = list(
+    timestamps = dynamic_env.get_timestamp(None)
-        dynamic_env.get_timestamp(index) for index in range(len(dynamic_env) // 2)
+    meta_model = LFNA_Meta(shape_container, args.layer_dim, args.time_dim, timestamps)
+    meta_model = meta_model.to(args.device)
+
+    logger.log("The base-model has {:} weights.".format(base_model.numel()))
+    logger.log("The meta-model has {:} weights.".format(meta_model.numel()))
+
+    batch_sampler = EnvSampler(dynamic_env, args.meta_batch, args.sampler_enlarge)
+    dynamic_env.reset_max_seq_length(args.seq_length)
+    """
+    env_loader = torch.utils.data.DataLoader(
+        dynamic_env,
+        batch_size=args.meta_batch,
+        shuffle=True,
+        num_workers=args.workers,
+        pin_memory=True,
+    )
+    """
+    env_loader = torch.utils.data.DataLoader(
+        dynamic_env,
+        batch_sampler=batch_sampler,
+        num_workers=args.workers,
+        pin_memory=True,
     )
 
-    hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim, timestamps)
+    optimizer = torch.optim.Adam(
-    hypernet = hypernet.to(args.device)
+        meta_model.parameters(), lr=args.init_lr, weight_decay=1e-5, amsgrad=True
-
+    )
-    import pdb
-
-    pdb.set_trace()
-
-    # task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
-    total_bar = 16
-    task_embeds = []
-    for i in range(total_bar):
-        tensor = torch.Tensor(1, args.task_dim).to(args.device)
-        task_embeds.append(torch.nn.Parameter(tensor))
-    for task_embed in task_embeds:
-        trunc_normal_(task_embed, std=0.02)
-
-    model.train()
-    hypernet.train()
-
-    parameters = list(hypernet.parameters()) + task_embeds
-    # optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
-    optimizer = torch.optim.Adam(parameters, lr=args.init_lr, weight_decay=1e-5)
     lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
         optimizer,
         milestones=[
@@ -77,71 +103,59 @@ def main(args):
         ],
         gamma=0.1,
     )
+    logger.log("The base-model is\n{:}".format(base_model))
+    logger.log("The meta-model is\n{:}".format(meta_model))
+    logger.log("The optimizer is\n{:}".format(optimizer))
+    logger.log("Per epoch iterations = {:}".format(len(env_loader)))
 
-    # total_bar = env_info["total"] - 1
     # LFNA meta-training
-    loss_meter = AverageMeter()
     per_epoch_time, start_time = AverageMeter(), time.time()
+    last_success_epoch = 0
     for iepoch in range(args.epochs):
 
-        need_time = "Time Left: {:}".format(
+        head_str = "[{:}] [{:04d}/{:04d}] ".format(
+            time_string(), iepoch, args.epochs
+        ) + "Time Left: {:}".format(
             convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
         )
-        head_str = (
+
-            "[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
+        loss_meter = epoch_train(
-            + need_time
+            env_loader,
+            meta_model,
+            base_model,
+            optimizer,
+            criterion,
+            args.device,
+            logger,
         )
-
-        losses = []
-        # for ibatch in range(args.meta_batch):
-        for cur_time in range(total_bar):
-            # cur_time = random.randint(0, total_bar)
-            cur_task_embed = task_embeds[cur_time]
-            cur_container = hypernet(cur_task_embed)
-            cur_x = env_info["{:}-x".format(cur_time)].to(args.device)
-            cur_y = env_info["{:}-y".format(cur_time)].to(args.device)
-            cur_dataset = TimeData(cur_time, cur_x, cur_y)
-
-            preds = model.forward_with_container(cur_dataset.x, cur_container)
-            optimizer.zero_grad()
-            loss = criterion(preds, cur_dataset.y)
-
-            losses.append(loss)
-
-        final_loss = torch.stack(losses).mean()
-        final_loss.backward()
-        optimizer.step()
         lr_scheduler.step()
-
-        loss_meter.update(final_loss.item())
-        if iepoch % 100 == 0:
         logger.log(
             head_str
-                + " meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
+            + " meta-loss: {meter.avg:.4f} ({meter.count:.0f})".format(meter=loss_meter)
-                    loss_meter.avg,
+            + " :: lr={:.5f}".format(min(lr_scheduler.get_last_lr()))
-                    loss_meter.val,
-                    min(lr_scheduler.get_last_lr()),
-                    len(losses),
         )
-            )
+        success, best_score = meta_model.save_best(-loss_meter.avg)
-
+        if success:
+            logger.log("Achieve the best with best_score = {:.3f}".format(best_score))
+            last_success_epoch = iepoch
             save_checkpoint(
                 {
-                    "hypernet": hypernet.state_dict(),
+                    "meta_model": meta_model.state_dict(),
-                    "task_embed": task_embed,
+                    "optimizer": optimizer.state_dict(),
                     "lr_scheduler": lr_scheduler.state_dict(),
                     "iepoch": iepoch,
+                    "args": args,
                 },
                 logger.path("model"),
                 logger,
             )
-            loss_meter.reset()
+        if iepoch - last_success_epoch >= args.early_stop_thresh:
+            logger.log("Early stop at {:}".format(iepoch))
+            break
+
         per_epoch_time.update(time.time() - start_time)
         start_time = time.time()
 
-    print(model)
-    print(hypernet)
-
     w_container_per_epoch = dict()
     for idx in range(0, total_bar):
         future_time = env_info["{:}-timestamp".format(idx)]
@@ -183,20 +197,26 @@ if __name__ == "__main__":
     parser.add_argument(
         "--hidden_dim",
         type=int,
-        required=True,
+        default=16,
         help="The hidden dimension.",
     )
     parser.add_argument(
         "--layer_dim",
         type=int,
-        required=True,
+        default=16,
-        help="The hidden dimension.",
+        help="The layer chunk dimension.",
+    )
+    parser.add_argument(
+        "--time_dim",
+        type=int,
+        default=16,
+        help="The timestamp dimension.",
     )
     #####
     parser.add_argument(
         "--init_lr",
         type=float,
-        default=0.1,
+        default=0.01,
         help="The initial learning rate for the optimizer (default is Adam)",
     )
     parser.add_argument(
@@ -206,10 +226,23 @@ if __name__ == "__main__":
         help="The batch size for the meta-model",
     )
     parser.add_argument(
-        "--epochs",
+        "--sampler_enlarge",
         type=int,
-        default=2000,
+        default=5,
-        help="The total number of epochs.",
+        help="Enlarge the #iterations for an epoch",
+    )
+    parser.add_argument("--epochs", type=int, default=1000, help="The total #epochs.")
+    parser.add_argument(
+        "--early_stop_thresh",
+        type=int,
+        default=50,
+        help="The maximum epochs for early stop.",
+    )
+    parser.add_argument(
+        "--seq_length", type=int, default=5, help="The sequence length."
+    )
+    parser.add_argument(
+        "--workers", type=int, default=4, help="The number of workers in parallel."
     )
     parser.add_argument(
         "--device",
@@ -223,8 +256,7 @@ if __name__ == "__main__":
     if args.rand_seed is None or args.rand_seed < 0:
         args.rand_seed = random.randint(1, 100000)
     assert args.save_dir is not None, "The save dir argument can not be None"
-    args.task_dim = args.layer_dim
+    args.save_dir = "{:}-{:}-d{:}_{:}_{:}".format(
-    args.save_dir = "{:}-{:}-d{:}".format(
+        args.save_dir, args.env_version, args.hidden_dim, args.layer_dim, args.time_dim
-        args.save_dir, args.env_version, args.hidden_dim
     )
     main(args)

exps/LFNA/lfna_meta_model.py

@@ -0,0 +1,128 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+import copy
+import torch
+
+import torch.nn.functional as F
+
+from xlayers import super_core
+from xlayers import trunc_normal_
+from models.xcore import get_model
+
+
+class LFNA_Meta(super_core.SuperModule):
+    """Learning to Forecast Neural Adaptation (Meta Model Design)."""
+
+    def __init__(
+        self,
+        shape_container,
+        layer_embeding,
+        time_embedding,
+        meta_timestamps,
+        mha_depth: int = 2,
+        dropout: float = 0.1,
+    ):
+        super(LFNA_Meta, self).__init__()
+        self._shape_container = shape_container
+        self._num_layers = len(shape_container)
+        self._numel_per_layer = []
+        for ilayer in range(self._num_layers):
+            self._numel_per_layer.append(shape_container[ilayer].numel())
+        self._raw_meta_timestamps = meta_timestamps
+
+        self.register_parameter(
+            "_super_layer_embed",
+            torch.nn.Parameter(torch.Tensor(self._num_layers, layer_embeding)),
+        )
+        self.register_parameter(
+            "_super_meta_embed",
+            torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_embedding)),
+        )
+        self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps))
+
+        # build transformer
+        layers = []
+        for ilayer in range(mha_depth):
+            layers.append(
+                super_core.SuperTransformerEncoderLayer(
+                    time_embedding,
+                    4,
+                    True,
+                    4,
+                    dropout,
+                    norm_affine=False,
+                    order=super_core.LayerOrder.PostNorm,
+                )
+            )
+        self.meta_corrector = super_core.SuperSequential(*layers)
+
+        model_kwargs = dict(
+            config=dict(model_type="dual_norm_mlp"),
+            input_dim=layer_embeding + time_embedding,
+            output_dim=max(self._numel_per_layer),
+            hidden_dims=[(layer_embeding + time_embedding) * 2] * 3,
+            act_cls="gelu",
+            norm_cls="layer_norm_1d",
+            dropout=dropout,
+        )
+        self._generator = get_model(**model_kwargs)
+        # print("generator: {:}".format(self._generator))
+
+        # unknown token
+        self.register_parameter(
+            "_unknown_token",
+            torch.nn.Parameter(torch.Tensor(1, time_embedding)),
+        )
+
+        # initialization
+        trunc_normal_(
+            [self._super_layer_embed, self._super_meta_embed, self._unknown_token],
+            std=0.02,
+        )
+
+    def forward_raw(self, timestamps):
+        # timestamps is a batch of sequence of timestamps
+        batch, seq = timestamps.shape
+        timestamps = timestamps.unsqueeze(dim=-1)
+        meta_timestamps = self._meta_timestamps.view(1, 1, -1)
+        time_diffs = timestamps - meta_timestamps
+        time_match_v, time_match_i = torch.min(torch.abs(time_diffs), dim=-1)
+        # select corresponding meta-knowledge
+        meta_match = torch.index_select(
+            self._super_meta_embed, dim=0, index=time_match_i.view(-1)
+        )
+        meta_match = meta_match.view(batch, seq, -1)
+        # create the probability
+        time_probs = (1 / torch.exp(time_match_v * 10)).view(batch, seq, 1)
+        time_probs[:, -1, :] = 0
+        unknown_token = self._unknown_token.view(1, 1, -1)
+        raw_meta_embed = time_probs * meta_match + (1 - time_probs) * unknown_token
+
+        meta_embed = self.meta_corrector(raw_meta_embed)
+        # create joint embed
+        num_layer, _ = self._super_layer_embed.shape
+        meta_embed = meta_embed.view(batch, seq, 1, -1).expand(-1, -1, num_layer, -1)
+        layer_embed = self._super_layer_embed.view(1, 1, num_layer, -1).expand(
+            batch, seq, -1, -1
+        )
+        joint_embed = torch.cat((meta_embed, layer_embed), dim=-1)
+        batch_weights = self._generator(joint_embed)
+        batch_containers = []
+        for seq_weights in torch.split(batch_weights, 1):
+            seq_containers = []
+            for weights in torch.split(seq_weights.squeeze(0), 1):
+                weights = torch.split(weights.squeeze(0), 1)
+                seq_containers.append(self._shape_container.translate(weights))
+            batch_containers.append(seq_containers)
+        return batch_containers
+
+    def forward_candidate(self, input):
+        raise NotImplementedError
+
+    def extra_repr(self) -> str:
+        return "(_super_layer_embed): {:}, (_super_meta_embed): {:}, (_meta_timestamps): {:}".format(
+            list(self._super_layer_embed.shape),
+            list(self._super_meta_embed.shape),
+            list(self._meta_timestamps.shape),
+        )

exps/LFNA/lfna_models_v2.py

@@ -1,72 +0,0 @@
-#####################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
-#####################################################
-import copy
-import torch
-
-import torch.nn.functional as F
-
-from xlayers import super_core
-from xlayers import trunc_normal_
-from models.xcore import get_model
-
-
-class HyperNet(super_core.SuperModule):
-    """The hyper-network."""
-
-    def __init__(
-        self,
-        shape_container,
-        layer_embeding,
-        task_embedding,
-        meta_timestamps,
-        return_container: bool = True,
-    ):
-        super(HyperNet, self).__init__()
-        self._shape_container = shape_container
-        self._num_layers = len(shape_container)
-        self._numel_per_layer = []
-        for ilayer in range(self._num_layers):
-            self._numel_per_layer.append(shape_container[ilayer].numel())
-
-        self.register_parameter(
-            "_super_layer_embed",
-            torch.nn.Parameter(torch.Tensor(self._num_layers, layer_embeding)),
-        )
-        trunc_normal_(self._super_layer_embed, std=0.02)
-
-        model_kwargs = dict(
-            config=dict(model_type="dual_norm_mlp"),
-            input_dim=layer_embeding + task_embedding,
-            output_dim=max(self._numel_per_layer),
-            hidden_dims=[(layer_embeding + task_embedding) * 2] * 3,
-            act_cls="gelu",
-            norm_cls="layer_norm_1d",
-            dropout=0.2,
-        )
-        import pdb
-
-        pdb.set_trace()
-        self._generator = get_model(**model_kwargs)
-        self._return_container = return_container
-        print("generator: {:}".format(self._generator))
-
-    def forward_raw(self, task_embed):
-        # task_embed = F.normalize(task_embed, dim=-1, p=2)
-        # layer_embed = F.normalize(self._super_layer_embed, dim=-1, p=2)
-        layer_embed = self._super_layer_embed
-        task_embed = task_embed.view(1, -1).expand(self._num_layers, -1)
-
-        joint_embed = torch.cat((task_embed, layer_embed), dim=-1)
-        weights = self._generator(joint_embed)
-        if self._return_container:
-            weights = torch.split(weights, 1)
-            return self._shape_container.translate(weights)
-        else:
-            return weights
-
-    def forward_candidate(self, input):
-        raise NotImplementedError
-
-    def extra_repr(self) -> str:
-        return "(_super_layer_embed): {:}".format(list(self._super_layer_embed.shape))

lib/datasets/synthetic_core.py

@@ -2,6 +2,7 @@
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.05 #
 #####################################################
 from .synthetic_utils import TimeStamp
+from .synthetic_env import EnvSampler
 from .synthetic_env import SyntheticDEnv
 from .math_core import LinearFunc
 from .math_core import DynamicLinearFunc

lib/datasets/synthetic_env.py

@@ -2,7 +2,7 @@
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
 #####################################################
 import math
-import abc
+import random
 import numpy as np
 from typing import List, Optional, Dict
 import torch
@@ -11,6 +11,28 @@ import torch.utils.data as data
 from .synthetic_utils import TimeStamp
 
 
+def is_list_tuple(x):
+    return isinstance(x, (tuple, list))
+
+
+def zip_sequence(sequence):
+    def _combine(*alist):
+        if is_list_tuple(alist[0]):
+            return [_combine(*xlist) for xlist in zip(*alist)]
+        else:
+            return torch.cat(alist, dim=0)
+
+    def unsqueeze(a):
+        if is_list_tuple(a):
+            return [unsqueeze(x) for x in a]
+        else:
+            return a.unsqueeze(dim=0)
+
+    with torch.no_grad():
+        sequence = [unsqueeze(a) for a in sequence]
+        return _combine(*sequence)
+
+
 class SyntheticDEnv(data.Dataset):
     """The synethtic dynamic environment."""
 
@@ -33,7 +55,7 @@ class SyntheticDEnv(data.Dataset):
         self._num_per_task = num_per_task
         if timestamp_config is None:
             timestamp_config = dict(mode=mode)
-        else:
+        elif "mode" not in timestamp_config:
             timestamp_config["mode"] = mode
 
         self._timestamp_generator = TimeStamp(**timestamp_config)
@@ -42,6 +64,7 @@ class SyntheticDEnv(data.Dataset):
         self._cov_functors = cov_functors
 
         self._oracle_map = None
+        self._seq_length = None
 
     @property
     def min_timestamp(self):
@@ -55,7 +78,16 @@ class SyntheticDEnv(data.Dataset):
     def timestamp_interval(self):
         return self._timestamp_generator.interval
 
+    def reset_max_seq_length(self, seq_length):
+        self._seq_length = seq_length
+
     def get_timestamp(self, index):
+        if index is None:
+            timestamps = []
+            for index in range(len(self._timestamp_generator)):
+                timestamps.append(self._timestamp_generator[index][1])
+            return tuple(timestamps)
+        else:
             index, timestamp = self._timestamp_generator[index]
             return timestamp
 
@@ -75,7 +107,14 @@ class SyntheticDEnv(data.Dataset):
     def __getitem__(self, index):
         assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
         index, timestamp = self._timestamp_generator[index]
+        if self._seq_length is None:
             return self.__call__(timestamp)
+        else:
+            timestamps = [
+                timestamp + i * self.timestamp_interval for i in range(self._seq_length)
+            ]
+            xdata = [self.__call__(timestamp) for timestamp in timestamps]
+            return zip_sequence(xdata)
 
     def __call__(self, timestamp):
         mean_list = [functor(timestamp) for functor in self._mean_functors]
@@ -88,10 +127,13 @@ class SyntheticDEnv(data.Dataset):
             mean_list, cov_matrix, size=self._num_per_task
         )
         if self._oracle_map is None:
-            return timestamp, torch.Tensor(dataset)
+            return torch.Tensor([timestamp]), torch.Tensor(dataset)
         else:
             targets = self._oracle_map.noise_call(dataset, timestamp)
-            return timestamp, (torch.Tensor(dataset), torch.Tensor(targets))
+            return torch.Tensor([timestamp]), (
+                torch.Tensor(dataset),
+                torch.Tensor(targets),
+            )
 
     def __len__(self):
         return len(self._timestamp_generator)
@@ -104,3 +146,20 @@ class SyntheticDEnv(data.Dataset):
             ndim=self._ndim,
             num_per_task=self._num_per_task,
         )
+
+
+class EnvSampler:
+    def __init__(self, env, batch, enlarge):
+        indexes = list(range(len(env)))
+        self._indexes = indexes * enlarge
+        self._batch = batch
+        self._iterations = len(self._indexes) // self._batch
+
+    def __iter__(self):
+        random.shuffle(self._indexes)
+        for it in range(self._iterations):
+            indexes = self._indexes[it * self._batch : (it + 1) * self._batch]
+            yield indexes
+
+    def __len__(self):
+        return self._iterations

lib/datasets/synthetic_utils.py

@@ -30,6 +30,7 @@ class UnifiedSplit:
             self._indexes = all_indexes[num_of_train + num_of_valid :]
         else:
             raise ValueError("Unkonwn mode of {:}".format(mode))
+        self._all_indexes = all_indexes
         self._mode = mode
 
     @property

lib/xlayers/super_rl_actor.py

@@ -1,120 +0,0 @@
-#####################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
-#####################################################
-# DISABLED / NOT-FINISHED
-#####################################################
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import math
-from typing import Optional, Callable
-
-import spaces
-from .super_container import SuperSequential
-from .super_linear import SuperLinear
-
-
-class SuperActor(SuperModule):
-    """A Actor in RL."""
-
-    def _distribution(self, obs):
-        raise NotImplementedError
-
-    def _log_prob_from_distribution(self, pi, act):
-        raise NotImplementedError
-
-    def forward_candidate(self, **kwargs):
-        return self.forward_raw(**kwargs)
-
-    def forward_raw(self, obs, act=None):
-        # Produce action distributions for given observations, and
-        # optionally compute the log likelihood of given actions under
-        # those distributions.
-        pi = self._distribution(obs)
-        logp_a = None
-        if act is not None:
-            logp_a = self._log_prob_from_distribution(pi, act)
-        return pi, logp_a
-
-
-class SuperLfnaMetaMLP(SuperModule):
-    def __init__(self, obs_dim, hidden_sizes, act_cls):
-        super(SuperLfnaMetaMLP).__init__()
-        self.delta_net = SuperSequential(
-            SuperLinear(obs_dim, hidden_sizes[0]),
-            act_cls(),
-            SuperLinear(hidden_sizes[0], hidden_sizes[1]),
-            act_cls(),
-            SuperLinear(hidden_sizes[1], 1),
-        )
-
-
-class SuperLfnaMetaMLP(SuperModule):
-    def __init__(self, obs_dim, act_dim, hidden_sizes, act_cls):
-        super(SuperLfnaMetaMLP).__init__()
-        log_std = -0.5 * np.ones(act_dim, dtype=np.float32)
-        self.log_std = torch.nn.Parameter(torch.as_tensor(log_std))
-        self.mu_net = SuperSequential(
-            SuperLinear(obs_dim, hidden_sizes[0]),
-            act_cls(),
-            SuperLinear(hidden_sizes[0], hidden_sizes[1]),
-            act_cls(),
-            SuperLinear(hidden_sizes[1], act_dim),
-        )
-
-    def _distribution(self, obs):
-        mu = self.mu_net(obs)
-        std = torch.exp(self.log_std)
-        return Normal(mu, std)
-
-    def _log_prob_from_distribution(self, pi, act):
-        return pi.log_prob(act).sum(axis=-1)
-
-    def forward_candidate(self, **kwargs):
-        return self.forward_raw(**kwargs)
-
-    def forward_raw(self, obs, act=None):
-        # Produce action distributions for given observations, and
-        # optionally compute the log likelihood of given actions under
-        # those distributions.
-        pi = self._distribution(obs)
-        logp_a = None
-        if act is not None:
-            logp_a = self._log_prob_from_distribution(pi, act)
-        return pi, logp_a
-
-
-class SuperMLPGaussianActor(SuperModule):
-    def __init__(self, obs_dim, act_dim, hidden_sizes, act_cls):
-        super(SuperMLPGaussianActor).__init__()
-        log_std = -0.5 * np.ones(act_dim, dtype=np.float32)
-        self.log_std = torch.nn.Parameter(torch.as_tensor(log_std))
-        self.mu_net = SuperSequential(
-            SuperLinear(obs_dim, hidden_sizes[0]),
-            act_cls(),
-            SuperLinear(hidden_sizes[0], hidden_sizes[1]),
-            act_cls(),
-            SuperLinear(hidden_sizes[1], act_dim),
-        )
-
-    def _distribution(self, obs):
-        mu = self.mu_net(obs)
-        std = torch.exp(self.log_std)
-        return Normal(mu, std)
-
-    def _log_prob_from_distribution(self, pi, act):
-        return pi.log_prob(act).sum(axis=-1)
-
-    def forward_candidate(self, **kwargs):
-        return self.forward_raw(**kwargs)
-
-    def forward_raw(self, obs, act=None):
-        # Produce action distributions for given observations, and
-        # optionally compute the log likelihood of given actions under
-        # those distributions.
-        pi = self._distribution(obs)
-        logp_a = None
-        if act is not None:
-            logp_a = self._log_prob_from_distribution(pi, act)
-        return pi, logp_a

lib/xlayers/super_transformer.py

@@ -42,6 +42,7 @@ class SuperTransformerEncoderLayer(SuperModule):
         qkv_bias: BoolSpaceType = False,
         mlp_hidden_multiplier: IntSpaceType = 4,
         drop: Optional[float] = None,
+        norm_affine: bool = True,
         act_layer: Callable[[], nn.Module] = nn.GELU,
         order: LayerOrder = LayerOrder.PreNorm,
     ):
@@ -62,19 +63,19 @@ class SuperTransformerEncoderLayer(SuperModule):
             drop=drop,
         )
         if order is LayerOrder.PreNorm:
-            self.norm1 = SuperLayerNorm1D(d_model)
+            self.norm1 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
             self.mha = mha
             self.drop1 = nn.Dropout(drop or 0.0)
-            self.norm2 = SuperLayerNorm1D(d_model)
+            self.norm2 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
             self.mlp = mlp
             self.drop2 = nn.Dropout(drop or 0.0)
         elif order is LayerOrder.PostNorm:
             self.mha = mha
             self.drop1 = nn.Dropout(drop or 0.0)
-            self.norm1 = SuperLayerNorm1D(d_model)
+            self.norm1 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
             self.mlp = mlp
             self.drop2 = nn.Dropout(drop or 0.0)
-            self.norm2 = SuperLayerNorm1D(d_model)
+            self.norm2 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
         else:
             raise ValueError("Unknown order: {:}".format(order))
         self._order = order

lib/xlayers/weight_init.py

@@ -60,4 +60,7 @@ def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
       >>> w = torch.empty(3, 5)
       >>> nn.init.trunc_normal_(w)
     """
+    if isinstance(tensor, list):
+        return [_no_grad_trunc_normal_(x, mean, std, a, b) for x in tensor]
+    else:
         return _no_grad_trunc_normal_(tensor, mean, std, a, b)

tests/test_synthetic_env.py

@@ -23,8 +23,16 @@ class TestSynethicEnv(unittest.TestCase):
     def test_simple(self):
         mean_generator = ComposedSinFunc(constant=0.1)
         std_generator = ConstantFunc(constant=0.5)
-
         dataset = SyntheticDEnv([mean_generator], [[std_generator]], num_per_task=5000)
         print(dataset)
         for timestamp, tau in dataset:
-            assert tau.shape == (5000, 1)
+            self.assertEqual(tau.shape, (5000, 1))
+
+    def test_length(self):
+        mean_generator = ComposedSinFunc(constant=0.1)
+        std_generator = ConstantFunc(constant=0.5)
+        dataset = SyntheticDEnv([mean_generator], [[std_generator]], num_per_task=5000)
+        self.assertEqual(len(dataset), 100)
+
+        dataset = SyntheticDEnv([mean_generator], [[std_generator]], mode="train")
+        self.assertEqual(len(dataset), 60)