Add more functions for synthetic env

exps/LFNA/lfna.py

@@ -0,0 +1,230 @@
+#####################################################
+# 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
+#####################################################
+import sys, time, copy, torch, random, argparse
+from tqdm import tqdm
+from copy import deepcopy
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
+from log_utils import time_string
+from log_utils import AverageMeter, convert_secs2time
+
+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 models.xcore import get_model
+from xlayers import super_core, trunc_normal_
+
+
+from lfna_utils import lfna_setup, train_model, TimeData
+
+from lfna_models_v2 import HyperNet
+
+
+def main(args):
+    logger, env_info, model_kwargs = lfna_setup(args)
+    dynamic_env = env_info["dynamic_env"]
+    model = get_model(**model_kwargs)
+    model = model.to(args.device)
+    criterion = torch.nn.MSELoss()
+
+    logger.log("There are {:} weights.".format(model.get_w_container().numel()))
+    # 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(
+        dynamic_env.get_timestamp(index) for index in range(len(dynamic_env) // 2)
+    )
+
+    hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim, timestamps)
+    hypernet = hypernet.to(args.device)
+
+    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=[
+            int(args.epochs * 0.8),
+            int(args.epochs * 0.9),
+        ],
+        gamma=0.1,
+    )
+
+    # total_bar = env_info["total"] - 1
+    # LFNA meta-training
+    loss_meter = AverageMeter()
+    per_epoch_time, start_time = AverageMeter(), time.time()
+    for iepoch in range(args.epochs):
+
+        need_time = "Time Left: {:}".format(
+            convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
+        )
+        head_str = (
+            "[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
+            + need_time
+        )
+
+        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(
+                    loss_meter.avg,
+                    loss_meter.val,
+                    min(lr_scheduler.get_last_lr()),
+                    len(losses),
+                )
+            )
+
+            save_checkpoint(
+                {
+                    "hypernet": hypernet.state_dict(),
+                    "task_embed": task_embed,
+                    "lr_scheduler": lr_scheduler.state_dict(),
+                    "iepoch": iepoch,
+                },
+                logger.path("model"),
+                logger,
+            )
+            loss_meter.reset()
+        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)]
+        future_x = env_info["{:}-x".format(idx)]
+        future_y = env_info["{:}-y".format(idx)]
+        future_container = hypernet(task_embeds[idx])
+        w_container_per_epoch[idx] = future_container.no_grad_clone()
+        with torch.no_grad():
+            future_y_hat = model.forward_with_container(
+                future_x, w_container_per_epoch[idx]
+            )
+            future_loss = criterion(future_y_hat, future_y)
+        logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
+
+    save_checkpoint(
+        {"w_container_per_epoch": w_container_per_epoch},
+        logger.path(None) / "final-ckp.pth",
+        logger,
+    )
+
+    logger.log("-" * 200 + "\n")
+    logger.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(".")
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        default="./outputs/lfna-synthetic/lfna-battle",
+        help="The checkpoint directory.",
+    )
+    parser.add_argument(
+        "--env_version",
+        type=str,
+        required=True,
+        help="The synthetic enviornment version.",
+    )
+    parser.add_argument(
+        "--hidden_dim",
+        type=int,
+        required=True,
+        help="The hidden dimension.",
+    )
+    parser.add_argument(
+        "--layer_dim",
+        type=int,
+        required=True,
+        help="The hidden dimension.",
+    )
+    #####
+    parser.add_argument(
+        "--init_lr",
+        type=float,
+        default=0.1,
+        help="The initial learning rate for the optimizer (default is Adam)",
+    )
+    parser.add_argument(
+        "--meta_batch",
+        type=int,
+        default=64,
+        help="The batch size for the meta-model",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=2000,
+        help="The total number of epochs.",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cpu",
+        help="",
+    )
+    # Random Seed
+    parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
+    args = parser.parse_args()
+    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, args.env_version, args.hidden_dim
+    )
+    main(args)

exps/LFNA/lfna_models_v2.py

@@ -0,0 +1,72 @@
+#####################################################
+# 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 * 4] * 3,
+            act_cls="gelu",
+            norm_cls="layer_norm_1d",
+            dropout=0.1,
+        )
+        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_env.py

@@ -55,6 +55,10 @@ class SyntheticDEnv(data.Dataset):
     def timestamp_interval(self):
         return self._timestamp_generator.interval
 
+    def get_timestamp(self, index):
+        index, timestamp = self._timestamp_generator[index]
+        return timestamp
+
     def set_oracle_map(self, functor):
         self._oracle_map = functor
 

lib/datasets/synthetic_utils.py

@@ -60,7 +60,7 @@ class TimeStamp(UnifiedSplit, data.Dataset):
     @property
     def max_timestamp(self):
         return self._max_timestamp
-  
+
     @property
     def interval(self):
         return self._interval