Update LFNA version 1.0
This commit is contained in:
D-X-Y
parent
3d3a04705f
commit
cfabd05de8
@ -1,7 +1,7 @@
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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.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 50000
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# python exps/LFNA/lfna.py --env_version v1
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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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@ -19,56 +19,82 @@ 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 datasets.synthetic_core import get_synthetic_env, EnvSampler
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from models.xcore import get_model
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from xlayers import super_core, trunc_normal_
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from lfna_utils import lfna_setup, train_model, TimeData
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from lfna_meta_model import LFNA_Meta
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from lfna_models_v2 import HyperNet
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def epoch_train(loader, meta_model, base_model, optimizer, criterion, device, logger):
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base_model.train()
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meta_model.train()
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loss_meter = AverageMeter()
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for ibatch, batch_data in enumerate(loader):
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timestamps, (batch_seq_inputs, batch_seq_targets) = batch_data
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timestamps = timestamps.squeeze(dim=-1).to(device)
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batch_seq_inputs = batch_seq_inputs.to(device)
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batch_seq_targets = batch_seq_targets.to(device)
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optimizer.zero_grad()
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batch_seq_containers = meta_model(timestamps)
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losses = []
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for seq_containers, seq_inputs, seq_targets in zip(
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batch_seq_containers, batch_seq_inputs, batch_seq_targets
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):
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for container, inputs, targets in zip(
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seq_containers, seq_inputs, seq_targets
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):
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predictions = base_model.forward_with_container(inputs, container)
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loss = criterion(predictions, targets)
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losses.append(loss)
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final_loss = torch.stack(losses).mean()
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final_loss.backward()
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optimizer.step()
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loss_meter.update(final_loss.item())
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return loss_meter
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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(**model_kwargs)
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model = model.to(args.device)
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dynamic_env = get_synthetic_env(mode="train", version=args.env_version)
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base_model = get_model(**model_kwargs)
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base_model = base_model.to(args.device)
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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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# meta_train_range = (dynamic_env.min_timestamp, (dynamic_env.min_timestamp + dynamic_env.max_timestamp) / 2)
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# meta_train_interval = dynamic_env.timestamp_interval
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shape_container = model.get_w_container().to_shape_container()
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shape_container = base_model.get_w_container().to_shape_container()
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# pre-train the hypernetwork
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timestamps = list(
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dynamic_env.get_timestamp(index) for index in range(len(dynamic_env) // 2)
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timestamps = dynamic_env.get_timestamp(None)
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meta_model = LFNA_Meta(shape_container, args.layer_dim, args.time_dim, timestamps)
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meta_model = meta_model.to(args.device)
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logger.log("The base-model has {:} weights.".format(base_model.numel()))
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logger.log("The meta-model has {:} weights.".format(meta_model.numel()))
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batch_sampler = EnvSampler(dynamic_env, args.meta_batch, args.sampler_enlarge)
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dynamic_env.reset_max_seq_length(args.seq_length)
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"""
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env_loader = torch.utils.data.DataLoader(
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dynamic_env,
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batch_size=args.meta_batch,
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shuffle=True,
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num_workers=args.workers,
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pin_memory=True,
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)
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"""
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env_loader = torch.utils.data.DataLoader(
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dynamic_env,
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batch_sampler=batch_sampler,
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num_workers=args.workers,
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pin_memory=True,
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)
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hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim, timestamps)
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hypernet = hypernet.to(args.device)
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import pdb
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pdb.set_trace()
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# task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
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total_bar = 16
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task_embeds = []
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for i in range(total_bar):
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tensor = torch.Tensor(1, args.task_dim).to(args.device)
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task_embeds.append(torch.nn.Parameter(tensor))
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for task_embed in task_embeds:
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trunc_normal_(task_embed, std=0.02)
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model.train()
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hypernet.train()
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parameters = list(hypernet.parameters()) + task_embeds
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# optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
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optimizer = torch.optim.Adam(parameters, lr=args.init_lr, weight_decay=1e-5)
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optimizer = torch.optim.Adam(
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meta_model.parameters(), lr=args.init_lr, weight_decay=1e-5, amsgrad=True
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)
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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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@ -77,71 +103,59 @@ def main(args):
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],
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gamma=0.1,
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)
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logger.log("The base-model is\n{:}".format(base_model))
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logger.log("The meta-model is\n{:}".format(meta_model))
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logger.log("The optimizer is\n{:}".format(optimizer))
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logger.log("Per epoch iterations = {:}".format(len(env_loader)))
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# total_bar = env_info["total"] - 1
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# LFNA meta-training
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loss_meter = AverageMeter()
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per_epoch_time, start_time = AverageMeter(), time.time()
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last_success_epoch = 0
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for iepoch in range(args.epochs):
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need_time = "Time Left: {:}".format(
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head_str = "[{:}] [{:04d}/{:04d}] ".format(
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time_string(), iepoch, args.epochs
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) + "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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head_str = (
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"[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
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+ need_time
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loss_meter = epoch_train(
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env_loader,
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meta_model,
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base_model,
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optimizer,
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criterion,
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args.device,
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logger,
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)
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losses = []
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# for ibatch in range(args.meta_batch):
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for cur_time in range(total_bar):
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# cur_time = random.randint(0, total_bar)
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cur_task_embed = task_embeds[cur_time]
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cur_container = hypernet(cur_task_embed)
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cur_x = env_info["{:}-x".format(cur_time)].to(args.device)
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cur_y = env_info["{:}-y".format(cur_time)].to(args.device)
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cur_dataset = TimeData(cur_time, cur_x, cur_y)
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preds = model.forward_with_container(cur_dataset.x, cur_container)
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optimizer.zero_grad()
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loss = criterion(preds, cur_dataset.y)
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losses.append(loss)
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final_loss = torch.stack(losses).mean()
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final_loss.backward()
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optimizer.step()
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lr_scheduler.step()
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loss_meter.update(final_loss.item())
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if iepoch % 100 == 0:
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logger.log(
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head_str
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+ " meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
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loss_meter.avg,
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loss_meter.val,
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min(lr_scheduler.get_last_lr()),
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len(losses),
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)
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)
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logger.log(
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head_str
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+ " meta-loss: {meter.avg:.4f} ({meter.count:.0f})".format(meter=loss_meter)
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+ " :: lr={:.5f}".format(min(lr_scheduler.get_last_lr()))
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)
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success, best_score = meta_model.save_best(-loss_meter.avg)
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if success:
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logger.log("Achieve the best with best_score = {:.3f}".format(best_score))
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last_success_epoch = iepoch
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save_checkpoint(
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{
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"hypernet": hypernet.state_dict(),
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"task_embed": task_embed,
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"meta_model": meta_model.state_dict(),
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"optimizer": optimizer.state_dict(),
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"lr_scheduler": lr_scheduler.state_dict(),
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"iepoch": iepoch,
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"args": args,
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},
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logger.path("model"),
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logger,
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)
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loss_meter.reset()
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if iepoch - last_success_epoch >= args.early_stop_thresh:
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logger.log("Early stop at {:}".format(iepoch))
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break
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per_epoch_time.update(time.time() - start_time)
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start_time = time.time()
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print(model)
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print(hypernet)
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w_container_per_epoch = dict()
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for idx in range(0, total_bar):
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future_time = env_info["{:}-timestamp".format(idx)]
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@ -183,20 +197,26 @@ if __name__ == "__main__":
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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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default=16,
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help="The hidden dimension.",
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)
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parser.add_argument(
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"--layer_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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default=16,
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help="The layer chunk dimension.",
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)
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parser.add_argument(
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"--time_dim",
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type=int,
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default=16,
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help="The timestamp 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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default=0.01,
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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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@ -206,10 +226,23 @@ if __name__ == "__main__":
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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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"--sampler_enlarge",
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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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default=5,
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help="Enlarge the #iterations for an epoch",
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)
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parser.add_argument("--epochs", type=int, default=1000, help="The total #epochs.")
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parser.add_argument(
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"--early_stop_thresh",
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type=int,
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default=50,
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help="The maximum epochs for early stop.",
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)
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parser.add_argument(
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"--seq_length", type=int, default=5, help="The sequence length."
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)
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parser.add_argument(
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"--workers", type=int, default=4, help="The number of workers in parallel."
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)
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parser.add_argument(
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"--device",
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@ -223,8 +256,7 @@ if __name__ == "__main__":
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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.task_dim = args.layer_dim
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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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args.save_dir = "{:}-{:}-d{:}_{:}_{:}".format(
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args.save_dir, args.env_version, args.hidden_dim, args.layer_dim, args.time_dim
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)
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main(args)
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128
exps/LFNA/lfna_meta_model.py
Normal file
128
exps/LFNA/lfna_meta_model.py
Normal file
@ -0,0 +1,128 @@
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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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import copy
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import torch
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import torch.nn.functional as F
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from xlayers import super_core
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from xlayers import trunc_normal_
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from models.xcore import get_model
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class LFNA_Meta(super_core.SuperModule):
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"""Learning to Forecast Neural Adaptation (Meta Model Design)."""
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def __init__(
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self,
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shape_container,
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layer_embeding,
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time_embedding,
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meta_timestamps,
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mha_depth: int = 2,
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dropout: float = 0.1,
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):
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super(LFNA_Meta, self).__init__()
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self._shape_container = shape_container
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self._num_layers = len(shape_container)
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self._numel_per_layer = []
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for ilayer in range(self._num_layers):
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self._numel_per_layer.append(shape_container[ilayer].numel())
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self._raw_meta_timestamps = meta_timestamps
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self.register_parameter(
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"_super_layer_embed",
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torch.nn.Parameter(torch.Tensor(self._num_layers, layer_embeding)),
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)
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self.register_parameter(
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"_super_meta_embed",
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torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_embedding)),
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)
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self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps))
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# build transformer
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layers = []
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for ilayer in range(mha_depth):
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layers.append(
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super_core.SuperTransformerEncoderLayer(
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time_embedding,
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4,
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True,
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4,
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dropout,
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norm_affine=False,
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order=super_core.LayerOrder.PostNorm,
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)
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)
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self.meta_corrector = super_core.SuperSequential(*layers)
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model_kwargs = dict(
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config=dict(model_type="dual_norm_mlp"),
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input_dim=layer_embeding + time_embedding,
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output_dim=max(self._numel_per_layer),
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hidden_dims=[(layer_embeding + time_embedding) * 2] * 3,
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act_cls="gelu",
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norm_cls="layer_norm_1d",
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dropout=dropout,
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)
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self._generator = get_model(**model_kwargs)
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# print("generator: {:}".format(self._generator))
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# unknown token
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self.register_parameter(
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"_unknown_token",
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torch.nn.Parameter(torch.Tensor(1, time_embedding)),
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)
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# initialization
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trunc_normal_(
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[self._super_layer_embed, self._super_meta_embed, self._unknown_token],
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std=0.02,
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)
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def forward_raw(self, timestamps):
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# timestamps is a batch of sequence of timestamps
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batch, seq = timestamps.shape
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timestamps = timestamps.unsqueeze(dim=-1)
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meta_timestamps = self._meta_timestamps.view(1, 1, -1)
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time_diffs = timestamps - meta_timestamps
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time_match_v, time_match_i = torch.min(torch.abs(time_diffs), dim=-1)
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# select corresponding meta-knowledge
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meta_match = torch.index_select(
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self._super_meta_embed, dim=0, index=time_match_i.view(-1)
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)
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meta_match = meta_match.view(batch, seq, -1)
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# create the probability
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time_probs = (1 / torch.exp(time_match_v * 10)).view(batch, seq, 1)
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time_probs[:, -1, :] = 0
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unknown_token = self._unknown_token.view(1, 1, -1)
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raw_meta_embed = time_probs * meta_match + (1 - time_probs) * unknown_token
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meta_embed = self.meta_corrector(raw_meta_embed)
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# create joint embed
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num_layer, _ = self._super_layer_embed.shape
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meta_embed = meta_embed.view(batch, seq, 1, -1).expand(-1, -1, num_layer, -1)
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layer_embed = self._super_layer_embed.view(1, 1, num_layer, -1).expand(
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batch, seq, -1, -1
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)
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joint_embed = torch.cat((meta_embed, layer_embed), dim=-1)
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batch_weights = self._generator(joint_embed)
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batch_containers = []
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for seq_weights in torch.split(batch_weights, 1):
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seq_containers = []
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for weights in torch.split(seq_weights.squeeze(0), 1):
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weights = torch.split(weights.squeeze(0), 1)
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seq_containers.append(self._shape_container.translate(weights))
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batch_containers.append(seq_containers)
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return batch_containers
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def forward_candidate(self, input):
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raise NotImplementedError
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def extra_repr(self) -> str:
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return "(_super_layer_embed): {:}, (_super_meta_embed): {:}, (_meta_timestamps): {:}".format(
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list(self._super_layer_embed.shape),
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list(self._super_meta_embed.shape),
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list(self._meta_timestamps.shape),
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)
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@ -1,72 +0,0 @@
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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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import copy
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import torch
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import torch.nn.functional as F
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from xlayers import super_core
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from xlayers import trunc_normal_
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from models.xcore import get_model
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class HyperNet(super_core.SuperModule):
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"""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 + task_embedding) * 2] * 3,
|
||||
act_cls="gelu",
|
||||
norm_cls="layer_norm_1d",
|
||||
dropout=0.2,
|
||||
)
|
||||
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))
|
||||
@ -225,8 +225,8 @@ def visualize_env(save_dir, version):
|
||||
def compare_algs(save_dir, version, alg_dir="./outputs/lfna-synthetic"):
|
||||
save_dir = Path(str(save_dir))
|
||||
for substr in ("pdf", "png"):
|
||||
sub_save_dir = save_dir / substr
|
||||
sub_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
sub_save_dir = save_dir / substr
|
||||
sub_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
dpi, width, height = 30, 3200, 2000
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.05 #
|
||||
#####################################################
|
||||
from .synthetic_utils import TimeStamp
|
||||
from .synthetic_env import EnvSampler
|
||||
from .synthetic_env import SyntheticDEnv
|
||||
from .math_core import LinearFunc
|
||||
from .math_core import DynamicLinearFunc
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
|
||||
#####################################################
|
||||
import math
|
||||
import abc
|
||||
import random
|
||||
import numpy as np
|
||||
from typing import List, Optional, Dict
|
||||
import torch
|
||||
@ -11,6 +11,28 @@ import torch.utils.data as data
|
||||
from .synthetic_utils import TimeStamp
|
||||
|
||||
|
||||
def is_list_tuple(x):
|
||||
return isinstance(x, (tuple, list))
|
||||
|
||||
|
||||
def zip_sequence(sequence):
|
||||
def _combine(*alist):
|
||||
if is_list_tuple(alist[0]):
|
||||
return [_combine(*xlist) for xlist in zip(*alist)]
|
||||
else:
|
||||
return torch.cat(alist, dim=0)
|
||||
|
||||
def unsqueeze(a):
|
||||
if is_list_tuple(a):
|
||||
return [unsqueeze(x) for x in a]
|
||||
else:
|
||||
return a.unsqueeze(dim=0)
|
||||
|
||||
with torch.no_grad():
|
||||
sequence = [unsqueeze(a) for a in sequence]
|
||||
return _combine(*sequence)
|
||||
|
||||
|
||||
class SyntheticDEnv(data.Dataset):
|
||||
"""The synethtic dynamic environment."""
|
||||
|
||||
@ -33,7 +55,7 @@ class SyntheticDEnv(data.Dataset):
|
||||
self._num_per_task = num_per_task
|
||||
if timestamp_config is None:
|
||||
timestamp_config = dict(mode=mode)
|
||||
else:
|
||||
elif "mode" not in timestamp_config:
|
||||
timestamp_config["mode"] = mode
|
||||
|
||||
self._timestamp_generator = TimeStamp(**timestamp_config)
|
||||
@ -42,6 +64,7 @@ class SyntheticDEnv(data.Dataset):
|
||||
self._cov_functors = cov_functors
|
||||
|
||||
self._oracle_map = None
|
||||
self._seq_length = None
|
||||
|
||||
@property
|
||||
def min_timestamp(self):
|
||||
@ -55,9 +78,18 @@ class SyntheticDEnv(data.Dataset):
|
||||
def timestamp_interval(self):
|
||||
return self._timestamp_generator.interval
|
||||
|
||||
def reset_max_seq_length(self, seq_length):
|
||||
self._seq_length = seq_length
|
||||
|
||||
def get_timestamp(self, index):
|
||||
index, timestamp = self._timestamp_generator[index]
|
||||
return timestamp
|
||||
if index is None:
|
||||
timestamps = []
|
||||
for index in range(len(self._timestamp_generator)):
|
||||
timestamps.append(self._timestamp_generator[index][1])
|
||||
return tuple(timestamps)
|
||||
else:
|
||||
index, timestamp = self._timestamp_generator[index]
|
||||
return timestamp
|
||||
|
||||
def set_oracle_map(self, functor):
|
||||
self._oracle_map = functor
|
||||
@ -75,7 +107,14 @@ class SyntheticDEnv(data.Dataset):
|
||||
def __getitem__(self, index):
|
||||
assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
|
||||
index, timestamp = self._timestamp_generator[index]
|
||||
return self.__call__(timestamp)
|
||||
if self._seq_length is None:
|
||||
return self.__call__(timestamp)
|
||||
else:
|
||||
timestamps = [
|
||||
timestamp + i * self.timestamp_interval for i in range(self._seq_length)
|
||||
]
|
||||
xdata = [self.__call__(timestamp) for timestamp in timestamps]
|
||||
return zip_sequence(xdata)
|
||||
|
||||
def __call__(self, timestamp):
|
||||
mean_list = [functor(timestamp) for functor in self._mean_functors]
|
||||
@ -88,10 +127,13 @@ class SyntheticDEnv(data.Dataset):
|
||||
mean_list, cov_matrix, size=self._num_per_task
|
||||
)
|
||||
if self._oracle_map is None:
|
||||
return timestamp, torch.Tensor(dataset)
|
||||
return torch.Tensor([timestamp]), torch.Tensor(dataset)
|
||||
else:
|
||||
targets = self._oracle_map.noise_call(dataset, timestamp)
|
||||
return timestamp, (torch.Tensor(dataset), torch.Tensor(targets))
|
||||
return torch.Tensor([timestamp]), (
|
||||
torch.Tensor(dataset),
|
||||
torch.Tensor(targets),
|
||||
)
|
||||
|
||||
def __len__(self):
|
||||
return len(self._timestamp_generator)
|
||||
@ -104,3 +146,20 @@ class SyntheticDEnv(data.Dataset):
|
||||
ndim=self._ndim,
|
||||
num_per_task=self._num_per_task,
|
||||
)
|
||||
|
||||
|
||||
class EnvSampler:
|
||||
def __init__(self, env, batch, enlarge):
|
||||
indexes = list(range(len(env)))
|
||||
self._indexes = indexes * enlarge
|
||||
self._batch = batch
|
||||
self._iterations = len(self._indexes) // self._batch
|
||||
|
||||
def __iter__(self):
|
||||
random.shuffle(self._indexes)
|
||||
for it in range(self._iterations):
|
||||
indexes = self._indexes[it * self._batch : (it + 1) * self._batch]
|
||||
yield indexes
|
||||
|
||||
def __len__(self):
|
||||
return self._iterations
|
||||
|
||||
@ -30,6 +30,7 @@ class UnifiedSplit:
|
||||
self._indexes = all_indexes[num_of_train + num_of_valid :]
|
||||
else:
|
||||
raise ValueError("Unkonwn mode of {:}".format(mode))
|
||||
self._all_indexes = all_indexes
|
||||
self._mode = mode
|
||||
|
||||
@property
|
||||
|
||||
@ -1,120 +0,0 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
|
||||
#####################################################
|
||||
# DISABLED / NOT-FINISHED
|
||||
#####################################################
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
import math
|
||||
from typing import Optional, Callable
|
||||
|
||||
import spaces
|
||||
from .super_container import SuperSequential
|
||||
from .super_linear import SuperLinear
|
||||
|
||||
|
||||
class SuperActor(SuperModule):
|
||||
"""A Actor in RL."""
|
||||
|
||||
def _distribution(self, obs):
|
||||
raise NotImplementedError
|
||||
|
||||
def _log_prob_from_distribution(self, pi, act):
|
||||
raise NotImplementedError
|
||||
|
||||
def forward_candidate(self, **kwargs):
|
||||
return self.forward_raw(**kwargs)
|
||||
|
||||
def forward_raw(self, obs, act=None):
|
||||
# Produce action distributions for given observations, and
|
||||
# optionally compute the log likelihood of given actions under
|
||||
# those distributions.
|
||||
pi = self._distribution(obs)
|
||||
logp_a = None
|
||||
if act is not None:
|
||||
logp_a = self._log_prob_from_distribution(pi, act)
|
||||
return pi, logp_a
|
||||
|
||||
|
||||
class SuperLfnaMetaMLP(SuperModule):
|
||||
def __init__(self, obs_dim, hidden_sizes, act_cls):
|
||||
super(SuperLfnaMetaMLP).__init__()
|
||||
self.delta_net = SuperSequential(
|
||||
SuperLinear(obs_dim, hidden_sizes[0]),
|
||||
act_cls(),
|
||||
SuperLinear(hidden_sizes[0], hidden_sizes[1]),
|
||||
act_cls(),
|
||||
SuperLinear(hidden_sizes[1], 1),
|
||||
)
|
||||
|
||||
|
||||
class SuperLfnaMetaMLP(SuperModule):
|
||||
def __init__(self, obs_dim, act_dim, hidden_sizes, act_cls):
|
||||
super(SuperLfnaMetaMLP).__init__()
|
||||
log_std = -0.5 * np.ones(act_dim, dtype=np.float32)
|
||||
self.log_std = torch.nn.Parameter(torch.as_tensor(log_std))
|
||||
self.mu_net = SuperSequential(
|
||||
SuperLinear(obs_dim, hidden_sizes[0]),
|
||||
act_cls(),
|
||||
SuperLinear(hidden_sizes[0], hidden_sizes[1]),
|
||||
act_cls(),
|
||||
SuperLinear(hidden_sizes[1], act_dim),
|
||||
)
|
||||
|
||||
def _distribution(self, obs):
|
||||
mu = self.mu_net(obs)
|
||||
std = torch.exp(self.log_std)
|
||||
return Normal(mu, std)
|
||||
|
||||
def _log_prob_from_distribution(self, pi, act):
|
||||
return pi.log_prob(act).sum(axis=-1)
|
||||
|
||||
def forward_candidate(self, **kwargs):
|
||||
return self.forward_raw(**kwargs)
|
||||
|
||||
def forward_raw(self, obs, act=None):
|
||||
# Produce action distributions for given observations, and
|
||||
# optionally compute the log likelihood of given actions under
|
||||
# those distributions.
|
||||
pi = self._distribution(obs)
|
||||
logp_a = None
|
||||
if act is not None:
|
||||
logp_a = self._log_prob_from_distribution(pi, act)
|
||||
return pi, logp_a
|
||||
|
||||
|
||||
class SuperMLPGaussianActor(SuperModule):
|
||||
def __init__(self, obs_dim, act_dim, hidden_sizes, act_cls):
|
||||
super(SuperMLPGaussianActor).__init__()
|
||||
log_std = -0.5 * np.ones(act_dim, dtype=np.float32)
|
||||
self.log_std = torch.nn.Parameter(torch.as_tensor(log_std))
|
||||
self.mu_net = SuperSequential(
|
||||
SuperLinear(obs_dim, hidden_sizes[0]),
|
||||
act_cls(),
|
||||
SuperLinear(hidden_sizes[0], hidden_sizes[1]),
|
||||
act_cls(),
|
||||
SuperLinear(hidden_sizes[1], act_dim),
|
||||
)
|
||||
|
||||
def _distribution(self, obs):
|
||||
mu = self.mu_net(obs)
|
||||
std = torch.exp(self.log_std)
|
||||
return Normal(mu, std)
|
||||
|
||||
def _log_prob_from_distribution(self, pi, act):
|
||||
return pi.log_prob(act).sum(axis=-1)
|
||||
|
||||
def forward_candidate(self, **kwargs):
|
||||
return self.forward_raw(**kwargs)
|
||||
|
||||
def forward_raw(self, obs, act=None):
|
||||
# Produce action distributions for given observations, and
|
||||
# optionally compute the log likelihood of given actions under
|
||||
# those distributions.
|
||||
pi = self._distribution(obs)
|
||||
logp_a = None
|
||||
if act is not None:
|
||||
logp_a = self._log_prob_from_distribution(pi, act)
|
||||
return pi, logp_a
|
||||
@ -42,6 +42,7 @@ class SuperTransformerEncoderLayer(SuperModule):
|
||||
qkv_bias: BoolSpaceType = False,
|
||||
mlp_hidden_multiplier: IntSpaceType = 4,
|
||||
drop: Optional[float] = None,
|
||||
norm_affine: bool = True,
|
||||
act_layer: Callable[[], nn.Module] = nn.GELU,
|
||||
order: LayerOrder = LayerOrder.PreNorm,
|
||||
):
|
||||
@ -62,19 +63,19 @@ class SuperTransformerEncoderLayer(SuperModule):
|
||||
drop=drop,
|
||||
)
|
||||
if order is LayerOrder.PreNorm:
|
||||
self.norm1 = SuperLayerNorm1D(d_model)
|
||||
self.norm1 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
|
||||
self.mha = mha
|
||||
self.drop1 = nn.Dropout(drop or 0.0)
|
||||
self.norm2 = SuperLayerNorm1D(d_model)
|
||||
self.norm2 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
|
||||
self.mlp = mlp
|
||||
self.drop2 = nn.Dropout(drop or 0.0)
|
||||
elif order is LayerOrder.PostNorm:
|
||||
self.mha = mha
|
||||
self.drop1 = nn.Dropout(drop or 0.0)
|
||||
self.norm1 = SuperLayerNorm1D(d_model)
|
||||
self.norm1 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
|
||||
self.mlp = mlp
|
||||
self.drop2 = nn.Dropout(drop or 0.0)
|
||||
self.norm2 = SuperLayerNorm1D(d_model)
|
||||
self.norm2 = SuperLayerNorm1D(d_model, elementwise_affine=norm_affine)
|
||||
else:
|
||||
raise ValueError("Unknown order: {:}".format(order))
|
||||
self._order = order
|
||||
|
||||
@ -60,4 +60,7 @@ def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
|
||||
>>> w = torch.empty(3, 5)
|
||||
>>> nn.init.trunc_normal_(w)
|
||||
"""
|
||||
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
|
||||
if isinstance(tensor, list):
|
||||
return [_no_grad_trunc_normal_(x, mean, std, a, b) for x in tensor]
|
||||
else:
|
||||
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
|
||||
|
||||
@ -23,8 +23,16 @@ class TestSynethicEnv(unittest.TestCase):
|
||||
def test_simple(self):
|
||||
mean_generator = ComposedSinFunc(constant=0.1)
|
||||
std_generator = ConstantFunc(constant=0.5)
|
||||
|
||||
dataset = SyntheticDEnv([mean_generator], [[std_generator]], num_per_task=5000)
|
||||
print(dataset)
|
||||
for timestamp, tau in dataset:
|
||||
assert tau.shape == (5000, 1)
|
||||
self.assertEqual(tau.shape, (5000, 1))
|
||||
|
||||
def test_length(self):
|
||||
mean_generator = ComposedSinFunc(constant=0.1)
|
||||
std_generator = ConstantFunc(constant=0.5)
|
||||
dataset = SyntheticDEnv([mean_generator], [[std_generator]], num_per_task=5000)
|
||||
self.assertEqual(len(dataset), 100)
|
||||
|
||||
dataset = SyntheticDEnv([mean_generator], [[std_generator]], mode="train")
|
||||
self.assertEqual(len(dataset), 60)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user