273 lines
9.5 KiB
Python
273 lines
9.5 KiB
Python
#####################################################
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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-v1.py
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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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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):
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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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def adapt(self, model, criterion, w_container, seq_datasets):
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w_container.requires_grad_(True)
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containers = [w_container]
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for idx, dataset in enumerate(seq_datasets):
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x, y = dataset.x, dataset.y
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y_hat = model.forward_with_container(x, containers[-1])
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loss = criterion(y_hat, y)
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gradients = torch.autograd.grad(loss, containers[-1].tensors)
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with torch.no_grad():
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flatten_w = containers[-1].flatten().view(-1, 1)
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flatten_g = containers[-1].flatten(gradients).view(-1, 1)
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input_statistics = torch.tensor([x.mean(), x.std()]).view(1, 2)
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input_statistics = input_statistics.expand(flatten_w.numel(), -1)
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delta_inputs = torch.cat((flatten_w, flatten_g, input_statistics), dim=-1)
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delta = self.delta_net(delta_inputs).view(-1)
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delta = torch.clamp(delta, -0.5, 0.5)
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unflatten_delta = containers[-1].unflatten(delta)
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future_container = containers[-1].no_grad_clone().additive(unflatten_delta)
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# future_container = containers[-1].additive(unflatten_delta)
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containers.append(future_container)
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# containers = containers[1:]
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meta_loss = []
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temp_containers = []
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for idx, dataset in enumerate(seq_datasets):
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if idx == 0:
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continue
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current_container = containers[idx]
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y_hat = model.forward_with_container(dataset.x, current_container)
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loss = criterion(y_hat, dataset.y)
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meta_loss.append(loss)
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temp_containers.append((dataset.timestamp, current_container, -loss.item()))
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meta_loss = sum(meta_loss)
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w_container.requires_grad_(False)
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# meta_loss.backward()
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# self.meta_optimizer.step()
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return meta_loss, temp_containers
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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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class TimeData:
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def __init__(self, timestamp, xs, ys):
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self._timestamp = timestamp
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self._xs = xs
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self._ys = ys
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@property
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def x(self):
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return self._xs
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@property
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def y(self):
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return self._ys
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@property
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def timestamp(self):
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return self._timestamp
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class Population:
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"""A population used to maintain models at different timestamps."""
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def __init__(self):
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self._time2model = dict()
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self._time2score = dict() # higher is better
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def append(self, timestamp, model, score):
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if timestamp in self._time2model:
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if self._time2score[timestamp] > score:
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return
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self._time2model[timestamp] = model.no_grad_clone()
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self._time2score[timestamp] = score
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def query(self, timestamp):
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closet_timestamp = None
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for xtime, model in self._time2model.items():
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if closet_timestamp is None or (
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xtime < timestamp and timestamp - closet_timestamp >= timestamp - xtime
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):
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closet_timestamp = xtime
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return self._time2model[closet_timestamp], closet_timestamp
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def debug_info(self, timestamps):
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xstrs = []
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for timestamp in timestamps:
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if timestamp in self._time2score:
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xstrs.append(
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"{:04d}: {:.4f}".format(timestamp, self._time2score[timestamp])
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)
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return ", ".join(xstrs)
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def main(args):
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prepare_seed(args.rand_seed)
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logger = prepare_logger(args)
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cache_path = (logger.path(None) / ".." / "env-info.pth").resolve()
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if cache_path.exists():
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env_info = torch.load(cache_path)
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else:
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env_info = dict()
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dynamic_env = get_synthetic_env()
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env_info["total"] = len(dynamic_env)
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for idx, (timestamp, (_allx, _ally)) in enumerate(tqdm(dynamic_env)):
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env_info["{:}-timestamp".format(idx)] = timestamp
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env_info["{:}-x".format(idx)] = _allx
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env_info["{:}-y".format(idx)] = _ally
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env_info["dynamic_env"] = dynamic_env
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torch.save(env_info, cache_path)
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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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base_model = get_model(
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dict(model_type="simple_mlp"),
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act_cls="leaky_relu",
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norm_cls="identity",
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input_dim=1,
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output_dim=1,
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)
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w_container = base_model.get_w_container()
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criterion = torch.nn.MSELoss()
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print("There are {:} weights.".format(w_container.numel()))
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adaptor = LFNAmlp(4, (50, 20), "leaky_relu")
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pool = Population()
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pool.append(0, w_container, -100)
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# LFNA meta-training
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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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debug_timestamp = set()
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all_meta_losses = []
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for ibatch in range(args.meta_batch):
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sampled_timestamp = random.randint(0, train_time_bar)
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query_w_container, query_timestamp = pool.query(sampled_timestamp)
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# def adapt(self, model, w_container, xs, ys):
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seq_datasets = []
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# xs, ys = [], []
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for it in range(sampled_timestamp, sampled_timestamp + args.max_seq):
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xs = env_info["{:}-x".format(it)]
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ys = env_info["{:}-y".format(it)]
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seq_datasets.append(TimeData(it, xs, ys))
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temp_meta_loss, temp_containers = adaptor.adapt(
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base_model, criterion, query_w_container, seq_datasets
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)
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all_meta_losses.append(temp_meta_loss)
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for temp_time, temp_container, temp_score in temp_containers:
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pool.append(temp_time, temp_container, temp_score)
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debug_timestamp.add(temp_time)
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meta_loss = torch.stack(all_meta_losses).mean()
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meta_loss.backward()
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adaptor.step()
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debug_str = pool.debug_info(debug_timestamp)
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logger.log("meta-loss: {:.4f}".format(meta_loss.item()))
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per_epoch_time.update(time.time() - start_time)
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start_time = time.time()
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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-v1",
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help="The checkpoint directory.",
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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=5,
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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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"--epochs",
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type=int,
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default=1000,
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help="The total number of epochs.",
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)
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parser.add_argument(
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"--max_seq",
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type=int,
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default=5,
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help="The maximum length of the sequence.",
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)
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parser.add_argument(
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"--workers",
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type=int,
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default=4,
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help="The number of data loading workers (default: 4)",
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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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main(args)
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