Update LFNA ablation codes
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D-X-Y
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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# python exps/LFNA/lfna-debug.py --env_version v1 --hidden_dim 16
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#####################################################
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import sys, time, copy, torch, random, argparse
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from tqdm import tqdm
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from copy import deepcopy
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from pathlib import Path
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
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from log_utils import time_string
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from log_utils import AverageMeter, convert_secs2time
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from utils import split_str2indexes
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from procedures.advanced_main import basic_train_fn, basic_eval_fn
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from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
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from datasets.synthetic_core import get_synthetic_env
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from models.xcore import get_model
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from xlayers import super_core
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from lfna_utils import lfna_setup, train_model, TimeData
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from lfna_models import HyperNet
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class LFNAmlp:
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"""A LFNA meta-model that uses the MLP as delta-net."""
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def __init__(self, obs_dim, hidden_sizes, act_name, criterion):
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self.delta_net = super_core.SuperSequential(
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super_core.SuperLinear(obs_dim, hidden_sizes[0]),
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super_core.super_name2activation[act_name](),
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super_core.SuperLinear(hidden_sizes[0], hidden_sizes[1]),
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super_core.super_name2activation[act_name](),
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super_core.SuperLinear(hidden_sizes[1], 1),
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)
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self.meta_optimizer = torch.optim.Adam(
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self.delta_net.parameters(), lr=0.01, amsgrad=True
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)
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self.criterion = criterion
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def adapt(self, model, seq_flatten_w):
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delta_inputs = torch.stack(seq_flatten_w, dim=-1)
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delta = self.delta_net(delta_inputs)
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container = model.get_w_container()
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unflatten_delta = container.unflatten(delta)
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future_container = container.create_container(unflatten_delta)
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return future_container
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def step(self):
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torch.nn.utils.clip_grad_norm_(self.delta_net.parameters(), 1.0)
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self.meta_optimizer.step()
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def zero_grad(self):
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self.meta_optimizer.zero_grad()
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self.delta_net.zero_grad()
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def state_dict(self):
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return dict(
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delta_net=self.delta_net.state_dict(),
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meta_optimizer=self.meta_optimizer.state_dict(),
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)
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def main(args):
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logger, env_info, model_kwargs = lfna_setup(args)
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dynamic_env = env_info["dynamic_env"]
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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total_time = env_info["total"]
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for i in range(total_time):
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for xkey in ("timestamp", "x", "y"):
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nkey = "{:}-{:}".format(i, xkey)
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assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
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train_time_bar = total_time // 2
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criterion = torch.nn.MSELoss()
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logger.log("There are {:} weights.".format(model.get_w_container().numel()))
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adaptor = LFNAmlp(args.meta_seq, (200, 200), "leaky_relu", criterion)
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# pre-train the model
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dataset = init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
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shape_container = model.get_w_container().to_shape_container()
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hypernet = HyperNet(shape_container, 16)
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optimizer = torch.optim.Adam(hypernet.parameters(), lr=args.init_lr, amsgrad=True)
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best_loss, best_param = None, None
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for _iepoch in range(args.epochs):
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container = hypernet(None)
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preds = model.forward_with_container(dataset.x, container)
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optimizer.zero_grad()
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loss = criterion(preds, dataset.y)
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loss.backward()
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optimizer.step()
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# save best
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if best_loss is None or best_loss > loss.item():
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best_loss = loss.item()
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best_param = copy.deepcopy(model.state_dict())
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print("hyper-net : best={:.4f}".format(best_loss))
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init_loss = train_model(model, init_dataset, args.init_lr, args.epochs)
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logger.log("The pre-training loss is {:.4f}".format(init_loss))
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import pdb
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pdb.set_trace()
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all_past_containers = []
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ground_truth_path = (
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logger.path(None) / ".." / "use-same-timestamp-v1-d16" / "final-ckp.pth"
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)
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ground_truth_data = torch.load(ground_truth_path)
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all_gt_containers = ground_truth_data["w_container_per_epoch"]
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all_gt_flattens = dict()
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for idx, container in all_gt_containers.items():
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all_gt_flattens[idx] = container.no_grad_clone().flatten()
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# LFNA meta-training
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meta_loss_meter = AverageMeter()
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per_epoch_time, start_time = AverageMeter(), time.time()
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for iepoch in range(args.epochs):
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need_time = "Time Left: {:}".format(
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convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
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)
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logger.log(
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"[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
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+ need_time
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)
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adaptor.zero_grad()
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meta_losses = []
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for ibatch in range(args.meta_batch):
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future_timestamp = random.randint(args.meta_seq, train_time_bar)
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future_dataset = TimeData(
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future_timestamp,
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env_info["{:}-x".format(future_timestamp)],
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env_info["{:}-y".format(future_timestamp)],
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)
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seq_datasets = []
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for iseq in range(args.meta_seq):
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cur_time = future_timestamp - iseq - 1
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cur_x = env_info["{:}-x".format(cur_time)]
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cur_y = env_info["{:}-y".format(cur_time)]
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seq_datasets.append(TimeData(cur_time, cur_x, cur_y))
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seq_datasets.reverse()
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seq_flatten_w = [
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all_gt_flattens[dataset.timestamp] for dataset in seq_datasets
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]
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future_container = adaptor.adapt(network, seq_flatten_w)
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"""
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future_y_hat = network.forward_with_container(
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future_dataset.x, future_container
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)
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future_loss = adaptor.criterion(future_y_hat, future_dataset.y)
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"""
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future_loss = adaptor.criterion(
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future_container.flatten(), all_gt_flattens[future_timestamp]
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)
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# import pdb; pdb.set_trace()
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meta_losses.append(future_loss)
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meta_loss = torch.stack(meta_losses).mean()
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meta_loss.backward()
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adaptor.step()
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meta_loss_meter.update(meta_loss.item())
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logger.log(
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"meta-loss: {:.4f} ({:.4f}) ".format(
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meta_loss_meter.avg, meta_loss_meter.val
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)
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)
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if iepoch % 200 == 0:
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save_checkpoint(
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{"adaptor": adaptor.state_dict(), "iepoch": iepoch},
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logger.path("model"),
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logger,
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)
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per_epoch_time.update(time.time() - start_time)
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start_time = time.time()
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w_container_per_epoch = dict()
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# import pdb; pdb.set_trace()
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for idx in range(1, env_info["total"]):
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future_time = env_info["{:}-timestamp".format(idx)]
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future_x = env_info["{:}-x".format(idx)]
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future_y = env_info["{:}-y".format(idx)]
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seq_datasets = []
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for iseq in range(1, args.meta_seq + 1):
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cur_time = future_timestamp - iseq - 1
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if cur_time < 0:
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cur_time = 0
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cur_x = env_info["{:}-x".format(cur_time)]
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cur_y = env_info["{:}-y".format(cur_time)]
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seq_datasets.append(TimeData(cur_time, cur_x, cur_y))
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seq_datasets.reverse()
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seq_flatten_w = [all_gt_flattens[dataset.timestamp] for dataset in seq_datasets]
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future_container = adaptor.adapt(network, seq_flatten_w)
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w_container_per_epoch[idx] = future_container.no_grad_clone()
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with torch.no_grad():
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future_y_hat = network.forward_with_container(
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future_x, w_container_per_epoch[idx]
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)
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future_loss = adaptor.criterion(future_y_hat, future_y)
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logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
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save_checkpoint(
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{"w_container_per_epoch": w_container_per_epoch},
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logger.path(None) / "final-ckp.pth",
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logger,
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)
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logger.log("-" * 200 + "\n")
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logger.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Use the data in the past.")
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parser.add_argument(
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"--save_dir",
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type=str,
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default="./outputs/lfna-synthetic/lfna-debug",
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help="The checkpoint directory.",
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)
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parser.add_argument(
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"--env_version",
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type=str,
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required=True,
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help="The synthetic enviornment version.",
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)
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parser.add_argument(
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"--hidden_dim",
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type=int,
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required=True,
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help="The hidden dimension.",
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)
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#####
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parser.add_argument(
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"--init_lr",
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type=float,
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default=0.1,
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help="The initial learning rate for the optimizer (default is Adam)",
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)
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parser.add_argument(
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"--meta_batch",
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type=int,
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default=32,
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help="The batch size for the meta-model",
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)
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parser.add_argument(
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"--meta_seq",
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type=int,
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default=10,
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help="The length of the sequence for meta-model.",
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)
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parser.add_argument(
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"--epochs",
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type=int,
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default=2000,
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help="The total number of epochs.",
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)
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# Random Seed
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parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
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args = parser.parse_args()
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if args.rand_seed is None or args.rand_seed < 0:
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args.rand_seed = random.randint(1, 100000)
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assert args.save_dir is not None, "The save dir argument can not be None"
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args.save_dir = "{:}-{:}-d{:}".format(
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args.save_dir, args.env_version, args.hidden_dim
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)
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main(args)
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