Update xlayers
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D-X-Y
@@ -21,6 +21,57 @@ 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, xs, ys):
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containers = [w_container]
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for idx, (x, y) in enumerate(zip(xs, ys)):
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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].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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for idx, (x, y) in enumerate(zip(xs, ys)):
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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(x, current_container)
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loss = criterion(y_hat, y)
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meta_loss.append(loss)
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meta_loss = sum(meta_loss)
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meta_loss.backward()
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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 Population:
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@@ -28,11 +79,23 @@ class Population:
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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):
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def append(self, timestamp, model, score):
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if timestamp in self._time2model:
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raise ValueError("This timestamp has been added.")
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self._time2model[timestamp] = model
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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 (
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closet_timestamp is None
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or 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 main(args):
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@@ -70,100 +133,39 @@ def main(args):
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)
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w_container = base_model.named_parameters_buffers()
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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)
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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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import pdb
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pdb.set_trace()
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print("-")
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for i, idx in enumerate(to_evaluate_indexes):
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need_time = "Time Left: {:}".format(
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convert_secs2time(
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per_timestamp_time.avg * (len(to_evaluate_indexes) - i), True
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)
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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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"[{:}]".format(time_string())
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+ " [{:04d}/{:04d}][{:04d}]".format(i, len(to_evaluate_indexes), idx)
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+ " "
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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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# train the same data
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assert idx != 0
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historical_x = env_info["{:}-x".format(idx)]
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historical_y = env_info["{:}-y".format(idx)]
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# build model
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mean, std = historical_x.mean().item(), historical_x.std().item()
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model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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# build optimizer
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optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
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criterion = torch.nn.MSELoss()
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lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
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optimizer,
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milestones=[
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int(args.epochs * 0.25),
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int(args.epochs * 0.5),
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int(args.epochs * 0.75),
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],
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gamma=0.3,
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)
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train_metric = MSEMetric()
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best_loss, best_param = None, None
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for _iepoch in range(args.epochs):
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preds = model(historical_x)
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optimizer.zero_grad()
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loss = criterion(preds, historical_y)
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loss.backward()
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optimizer.step()
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lr_scheduler.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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model.load_state_dict(best_param)
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with torch.no_grad():
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train_metric(preds, historical_y)
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train_results = train_metric.get_info()
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metric = ComposeMetric(MSEMetric(), SaveMetric())
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eval_dataset = torch.utils.data.TensorDataset(
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env_info["{:}-x".format(idx)], env_info["{:}-y".format(idx)]
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)
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eval_loader = torch.utils.data.DataLoader(
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eval_dataset, batch_size=args.batch_size, shuffle=False, num_workers=0
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)
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results = basic_eval_fn(eval_loader, model, metric, logger)
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log_str = (
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"[{:}]".format(time_string())
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+ " [{:04d}/{:04d}]".format(idx, env_info["total"])
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+ " train-mse: {:.5f}, eval-mse: {:.5f}".format(
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train_results["mse"], results["mse"]
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)
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)
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logger.log(log_str)
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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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xs, ys = [], []
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for it in range(sampled_timestamp, sampled_timestamp + args.max_seq):
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xs.append(env_info["{:}-x".format(it)])
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ys.append(env_info["{:}-y".format(it)])
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adaptor.adapt(base_model, criterion, query_w_container, xs, ys)
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import pdb
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save_path = logger.path(None) / "{:04d}-{:04d}.pth".format(
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idx, env_info["total"]
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)
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save_checkpoint(
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{
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"model_state_dict": model.state_dict(),
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"model": model,
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"index": idx,
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"timestamp": env_info["{:}-timestamp".format(idx)],
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},
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save_path,
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logger,
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)
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pdb.set_trace()
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print("-")
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logger.log("")
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per_timestamp_time.update(time.time() - start_time)
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@@ -188,10 +190,10 @@ if __name__ == "__main__":
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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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"--batch_size",
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"--meta_batch",
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type=int,
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default=512,
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help="The batch size",
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default=2,
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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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@@ -199,6 +201,12 @@ if __name__ == "__main__":
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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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