Updates

exps/LFNA/lfna-debug.py

@@ -0,0 +1,476 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+# python exps/LFNA/lfna-debug.py --env_version v1 --workers 0
+# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.001
+# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.002
+#####################################################
+import sys, time, copy, torch, random, argparse
+from tqdm import tqdm
+from copy import deepcopy
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / ".." / "..").resolve()
+print("LIB-DIR: {:}".format(lib_dir))
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+
+from xautodl.procedures import (
+    prepare_seed,
+    prepare_logger,
+    save_checkpoint,
+    copy_checkpoint,
+)
+from xautodl.log_utils import time_string
+from xautodl.log_utils import AverageMeter, convert_secs2time
+
+from xautodl.utils import split_str2indexes
+
+from xautodl.procedures.advanced_main import basic_train_fn, basic_eval_fn
+from xautodl.procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
+from xautodl.datasets.synthetic_core import get_synthetic_env, EnvSampler
+from xautodl.models.xcore import get_model
+from xautodl.xlayers import super_core, trunc_normal_
+
+from lfna_utils import lfna_setup, train_model, TimeData
+from lfna_meta_model import LFNA_Meta
+
+
+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 epoch_evaluate(loader, meta_model, base_model, criterion, device, logger):
+    with torch.no_grad():
+        base_model.eval()
+        meta_model.eval()
+        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)
+
+            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()
+            loss_meter.update(final_loss.item())
+    return loss_meter
+
+
+def pretrain(base_model, meta_model, criterion, xenv, args, logger):
+    base_model.train()
+    meta_model.train()
+
+    optimizer = torch.optim.Adam(
+        meta_model.parameters(),
+        lr=args.lr,
+        weight_decay=args.weight_decay,
+        amsgrad=True,
+    )
+    logger.log("Pre-train the meta-model")
+    logger.log("Using the optimizer: {:}".format(optimizer))
+
+    meta_model.set_best_dir(logger.path(None) / "ckps-basic-pretrain")
+    rand_index = random.randint(0, meta_model.meta_length - xenv.seq_length - 1)
+    for iepoch in range(args.epochs):
+        left_time = "Time Left: {:}".format(
+            convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
+        )
+        losses = []
+        for ibatch in range(args.meta_batch):
+            timestamps = meta_model.meta_timestamps[
+                rand_index : rand_index + xenv.seq_length
+            ]
+            seq_timestamps, (seq_inputs, seq_targets) = xenv.seq_call(timestamps)
+            time_embeds = meta_model.super_meta_embed[
+                rand_index : rand_index + xenv.seq_length
+            ]
+            [seq_containers], time_embeds = meta_model(
+                None, torch.unsqueeze(time_embeds, dim=0)
+            )
+            seq_inputs, seq_targets = seq_inputs.to(args.device), seq_targets.to(
+                args.device
+            )
+            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()
+        # success
+        success, best_score = meta_model.save_best(-final_loss.item())
+        logger.log(
+            "{:} [{:04d}/{:}] loss : {:.5f}".format(
+                time_string(),
+                iepoch,
+                args.epochs,
+                final_loss.item(),
+            )
+            + ", batch={:}".format(len(losses))
+            + ", success={:}, best_score={:.4f}".format(success, -best_score)
+            + " {:}".format(left_time)
+        )
+        per_epoch_time.update(time.time() - start_time)
+        start_time = time.time()
+
+
+def main(args):
+    logger, env_info, model_kwargs = lfna_setup(args)
+    train_env = get_synthetic_env(mode="train", version=args.env_version)
+    valid_env = get_synthetic_env(mode="valid", version=args.env_version)
+    logger.log("training enviornment: {:}".format(train_env))
+    logger.log("validation enviornment: {:}".format(valid_env))
+
+    base_model = get_model(**model_kwargs)
+    base_model = base_model.to(args.device)
+    criterion = torch.nn.MSELoss()
+
+    shape_container = base_model.get_w_container().to_shape_container()
+
+    # pre-train the hypernetwork
+    timestamps = train_env.get_timestamp(None)
+    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(train_env, args.meta_batch, args.sampler_enlarge)
+    train_env.reset_max_seq_length(args.seq_length)
+    valid_env.reset_max_seq_length(args.seq_length)
+    valid_env_loader = torch.utils.data.DataLoader(
+        valid_env,
+        batch_size=args.meta_batch,
+        shuffle=True,
+        num_workers=args.workers,
+        pin_memory=True,
+    )
+    train_env_loader = torch.utils.data.DataLoader(
+        train_env,
+        batch_sampler=batch_sampler,
+        num_workers=args.workers,
+        pin_memory=True,
+    )
+
+    optimizer = torch.optim.Adam(
+        meta_model.parameters(),
+        lr=args.lr,
+        weight_decay=args.weight_decay,
+        amsgrad=True,
+    )
+    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
+        optimizer,
+        milestones=[1, 2, 3, 4, 5],
+        gamma=0.2,
+    )
+    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("The scheduler is\n{:}".format(lr_scheduler))
+    logger.log("Per epoch iterations = {:}".format(len(train_env_loader)))
+
+    pretrain(base_model, meta_model, criterion, train_env, args, logger)
+
+    if logger.path("model").exists():
+        ckp_data = torch.load(logger.path("model"))
+        base_model.load_state_dict(ckp_data["base_model"])
+        meta_model.load_state_dict(ckp_data["meta_model"])
+        optimizer.load_state_dict(ckp_data["optimizer"])
+        lr_scheduler.load_state_dict(ckp_data["lr_scheduler"])
+        last_success_epoch = ckp_data["last_success_epoch"]
+        start_epoch = ckp_data["iepoch"] + 1
+        check_strs = [
+            "epochs",
+            "env_version",
+            "hidden_dim",
+            "lr",
+            "layer_dim",
+            "time_dim",
+            "seq_length",
+        ]
+        for xstr in check_strs:
+            cx = getattr(args, xstr)
+            px = getattr(ckp_data["args"], xstr)
+            assert cx == px, "[{:}] {:} vs {:}".format(xstr, cx, ps)
+        success, _ = meta_model.save_best(ckp_data["cur_score"])
+        logger.log("Load ckp from {:}".format(logger.path("model")))
+        if success:
+            logger.log(
+                "Re-save the best model with score={:}".format(ckp_data["cur_score"])
+            )
+    else:
+        start_epoch, last_success_epoch = 0, 0
+
+    # LFNA meta-train
+    meta_model.set_best_dir(logger.path(None) / "checkpoint")
+    per_epoch_time, start_time = AverageMeter(), time.time()
+    for iepoch in range(start_epoch, args.epochs):
+
+        head_str = "[{:}] [{:04d}/{:04d}] ".format(
+            time_string(), iepoch, args.epochs
+        ) + "Time Left: {:}".format(
+            convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
+        )
+
+        loss_meter = epoch_train(
+            train_env_loader,
+            meta_model,
+            base_model,
+            optimizer,
+            criterion,
+            args.device,
+            logger,
+        )
+
+        valid_loss_meter = epoch_evaluate(
+            valid_env_loader, meta_model, base_model, criterion, args.device, logger
+        )
+        logger.log(
+            head_str
+            + " meta-train-loss: {meter.avg:.4f} ({meter.count:.0f})".format(
+                meter=loss_meter
+            )
+            + " meta-valid-loss: {meter.val:.4f}".format(meter=valid_loss_meter)
+            + " :: lr={:.5f}".format(min(lr_scheduler.get_last_lr()))
+            + "  :: last-success={:}".format(last_success_epoch)
+        )
+        success, best_score = meta_model.save_best(-loss_meter.avg)
+        if success:
+            logger.log("Achieve the best with best-score = {:.5f}".format(best_score))
+            last_success_epoch = iepoch
+            save_checkpoint(
+                {
+                    "meta_model": meta_model.state_dict(),
+                    "base_model": base_model.state_dict(),
+                    "optimizer": optimizer.state_dict(),
+                    "lr_scheduler": lr_scheduler.state_dict(),
+                    "last_success_epoch": last_success_epoch,
+                    "cur_score": -loss_meter.avg,
+                    "iepoch": iepoch,
+                    "args": args,
+                },
+                logger.path("model"),
+                logger,
+            )
+        if iepoch - last_success_epoch >= args.early_stop_thresh:
+            if lr_scheduler.last_epoch > 4:
+                logger.log("Early stop at {:}".format(iepoch))
+                break
+            else:
+                last_success_epoch = iepoch
+                lr_scheduler.step()
+                logger.log("Decay the lr [{:}]".format(lr_scheduler.last_epoch))
+
+        per_epoch_time.update(time.time() - start_time)
+        start_time = time.time()
+
+    # meta-test
+    meta_model.load_best()
+    eval_env = env_info["dynamic_env"]
+    w_container_per_epoch = dict()
+    for idx in range(args.seq_length, len(eval_env)):
+        # build-timestamp
+        future_time = env_info["{:}-timestamp".format(idx)].item()
+        time_seqs = []
+        for iseq in range(args.seq_length):
+            time_seqs.append(future_time - iseq * eval_env.timestamp_interval)
+        time_seqs.reverse()
+        with torch.no_grad():
+            meta_model.eval()
+            base_model.eval()
+            time_seqs = torch.Tensor(time_seqs).view(1, -1).to(args.device)
+            [seq_containers] = meta_model(time_seqs)
+            future_container = seq_containers[-1]
+            w_container_per_epoch[idx] = future_container.no_grad_clone()
+            # evaluation
+            future_x = env_info["{:}-x".format(idx)].to(args.device)
+            future_y = env_info["{:}-y".format(idx)].to(args.device)
+            future_y_hat = base_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())
+            )
+
+        # creating the new meta-time-embedding
+        distance = meta_model.get_closest_meta_distance(future_time)
+        if distance < eval_env.timestamp_interval:
+            continue
+        #
+        new_param = meta_model.create_meta_embed()
+        optimizer = torch.optim.Adam(
+            [new_param], lr=args.refine_lr, weight_decay=1e-5, amsgrad=True
+        )
+        meta_model.replace_append_learnt(
+            torch.Tensor([future_time]).to(args.device), new_param
+        )
+        meta_model.eval()
+        base_model.train()
+        for iepoch in range(args.refine_epochs):
+            optimizer.zero_grad()
+            [seq_containers] = meta_model(time_seqs)
+            future_container = seq_containers[-1]
+            future_y_hat = base_model.forward_with_container(future_x, future_container)
+            future_loss = criterion(future_y_hat, future_y)
+            future_loss.backward()
+            optimizer.step()
+        logger.log(
+            "post-meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item())
+        )
+        with torch.no_grad():
+            meta_model.replace_append_learnt(None, None)
+            meta_model.append_fixed(torch.Tensor([future_time]), new_param)
+
+    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,
+        default=16,
+        help="The hidden dimension.",
+    )
+    parser.add_argument(
+        "--layer_dim",
+        type=int,
+        default=16,
+        help="The layer chunk dimension.",
+    )
+    parser.add_argument(
+        "--time_dim",
+        type=int,
+        default=16,
+        help="The timestamp dimension.",
+    )
+    #####
+    parser.add_argument(
+        "--lr",
+        type=float,
+        default=0.002,
+        help="The initial learning rate for the optimizer (default is Adam)",
+    )
+    parser.add_argument(
+        "--weight_decay",
+        type=float,
+        default=0.00001,
+        help="The weight decay 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(
+        "--sampler_enlarge",
+        type=int,
+        default=5,
+        help="Enlarge the #iterations for an epoch",
+    )
+    parser.add_argument("--epochs", type=int, default=10000, help="The total #epochs.")
+    parser.add_argument(
+        "--refine_lr",
+        type=float,
+        default=0.005,
+        help="The learning rate for the optimizer, during refine",
+    )
+    parser.add_argument(
+        "--refine_epochs", type=int, default=1000, help="The final refine #epochs."
+    )
+    parser.add_argument(
+        "--early_stop_thresh",
+        type=int,
+        default=20,
+        help="The #epochs for early stop.",
+    )
+    parser.add_argument(
+        "--seq_length", type=int, default=10, help="The sequence length."
+    )
+    parser.add_argument(
+        "--workers", type=int, default=4, help="The number of workers in parallel."
+    )
+    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.save_dir = "{:}-d{:}_{:}_{:}-s{:}-lr{:}-wd{:}-e{:}-env{:}".format(
+        args.save_dir,
+        args.hidden_dim,
+        args.layer_dim,
+        args.time_dim,
+        args.seq_length,
+        args.lr,
+        args.weight_decay,
+        args.epochs,
+        args.env_version,
+    )
+    main(args)

exps/LFNA/lfna.py

@@ -93,7 +93,7 @@ def epoch_evaluate(loader, meta_model, base_model, criterion, device, logger):
     return loss_meter
 
 
-def pretrain(base_model, meta_model, criterion, xenv, args, logger):
+def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
     optimizer = torch.optim.Adam(
         meta_model.parameters(),
         lr=args.lr,
@@ -164,6 +164,69 @@ def pretrain(base_model, meta_model, criterion, xenv, args, logger):
         start_time = time.time()
 
 
+def pretrain(base_model, meta_model, criterion, xenv, args, logger):
+    base_model.train()
+    meta_model.train()
+    optimizer = torch.optim.Adam(
+        meta_model.parameters(),
+        lr=args.lr,
+        weight_decay=args.weight_decay,
+        amsgrad=True,
+    )
+    logger.log("Pre-train the meta-model")
+    logger.log("Using the optimizer: {:}".format(optimizer))
+
+    meta_model.set_best_dir(logger.path(None) / "ckps-basic-pretrain")
+    meta_model.set_best_name("pretrain-{:}.pth".format(args.rand_seed))
+    per_epoch_time, start_time = AverageMeter(), time.time()
+    for iepoch in range(args.epochs):
+        left_time = "Time Left: {:}".format(
+            convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
+        )
+        losses = []
+        optimizer.zero_grad()
+        for ibatch in range(args.meta_batch):
+            rand_index = random.randint(0, meta_model.meta_length - xenv.seq_length - 1)
+            timestamps = meta_model.meta_timestamps[
+                rand_index : rand_index + xenv.seq_length
+            ]
+
+            seq_timestamps, (seq_inputs, seq_targets) = xenv.seq_call(timestamps)
+            time_embeds = meta_model.super_meta_embed[
+                rand_index : rand_index + xenv.seq_length
+            ]
+            [seq_containers], time_embeds = meta_model(
+                None, torch.unsqueeze(time_embeds, dim=0)
+            )
+            seq_inputs, seq_targets = seq_inputs.to(args.device), seq_targets.to(
+                args.device
+            )
+            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()
+        # success
+        success, best_score = meta_model.save_best(-final_loss.item())
+        logger.log(
+            "{:} [{:04d}/{:}] loss : {:.5f}".format(
+                time_string(),
+                iepoch,
+                args.epochs,
+                final_loss.item(),
+            )
+            + ", batch={:}".format(len(losses))
+            + ", success={:}, best_score={:.4f}".format(success, -best_score)
+            + " {:}".format(left_time)
+        )
+        per_epoch_time.update(time.time() - start_time)
+        start_time = time.time()
+
+
 def main(args):
     logger, env_info, model_kwargs = lfna_setup(args)
     train_env = get_synthetic_env(mode="train", version=args.env_version)

exps/LFNA/lfna_meta_model.py

@@ -85,7 +85,6 @@ class LFNA_Meta(super_core.SuperModule):
             dropout=dropout,
         )
         self._generator = get_model(**model_kwargs)
-        # print("generator: {:}".format(self._generator))
 
         # initialization
         trunc_normal_(
@@ -163,9 +162,12 @@ class LFNA_Meta(super_core.SuperModule):
         corrected_embeds = self.meta_corrector(init_timestamp_embeds)
         return corrected_embeds
 
-    def forward_raw(self, timestamps):
-        batch, seq = timestamps.shape
-        time_embed = self._obtain_time_embed(timestamps)
+    def forward_raw(self, timestamps, time_embed):
+        if time_embed is None:
+            batch, seq = timestamps.shape
+            time_embed = self._obtain_time_embed(timestamps)
+        else:
+            batch, seq, _ = time_embed.shape
         # create joint embed
         num_layer, _ = self._super_layer_embed.shape
         meta_embed = time_embed.view(batch, seq, 1, -1).expand(-1, -1, num_layer, -1)