Fix bugs in ViT
This commit is contained in:
D-X-Y
294
exps/basic/xmain.py
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294
exps/basic/xmain.py
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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#####################################################
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import sys, time, torch, random, argparse
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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from copy import deepcopy
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from pathlib import Path
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from xautodl.datasets import get_datasets
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from xautodl.config_utils import load_config, obtain_basic_args as obtain_args
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from xautodl.procedures import (
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prepare_seed,
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prepare_logger,
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save_checkpoint,
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copy_checkpoint,
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)
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from xautodl.procedures import get_optim_scheduler, get_procedures
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from xautodl.models import obtain_model
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from xautodl.xmodels import obtain_model as obtain_xmodel
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from xautodl.nas_infer_model import obtain_nas_infer_model
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from xautodl.utils import get_model_infos
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from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
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def main(args):
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assert torch.cuda.is_available(), "CUDA is not available."
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = True
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# torch.backends.cudnn.deterministic = True
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# torch.set_num_threads(args.workers)
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prepare_seed(args.rand_seed)
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logger = prepare_logger(args)
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train_data, valid_data, xshape, class_num = get_datasets(
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args.dataset, args.data_path, args.cutout_length
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)
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=args.batch_size,
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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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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=args.batch_size,
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shuffle=False,
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num_workers=args.workers,
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pin_memory=True,
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)
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# get configures
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model_config = load_config(args.model_config, {"class_num": class_num}, logger)
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optim_config = load_config(args.optim_config, {"class_num": class_num}, logger)
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if args.model_source == "normal":
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base_model = obtain_model(model_config)
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elif args.model_source == "nas":
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base_model = obtain_nas_infer_model(model_config, args.extra_model_path)
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elif args.model_source == "autodl-searched":
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base_model = obtain_model(model_config, args.extra_model_path)
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elif args.model_source in ("x", "xmodel"):
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base_model = obtain_xmodel(model_config)
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else:
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raise ValueError("invalid model-source : {:}".format(args.model_source))
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flop, param = get_model_infos(base_model, xshape)
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logger.log("model ====>>>>:\n{:}".format(base_model))
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logger.log("model information : {:}".format(base_model.get_message()))
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logger.log("-" * 50)
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logger.log(
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"Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(
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param, flop, flop / 1e3
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)
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)
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logger.log("-" * 50)
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logger.log("train_data : {:}".format(train_data))
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logger.log("valid_data : {:}".format(valid_data))
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optimizer, scheduler, criterion = get_optim_scheduler(
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base_model.parameters(), optim_config
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)
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logger.log("optimizer : {:}".format(optimizer))
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logger.log("scheduler : {:}".format(scheduler))
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logger.log("criterion : {:}".format(criterion))
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last_info, model_base_path, model_best_path = (
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logger.path("info"),
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logger.path("model"),
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logger.path("best"),
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)
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network, criterion = torch.nn.DataParallel(base_model).cuda(), criterion.cuda()
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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start".format(last_info)
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)
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last_infox = torch.load(last_info)
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start_epoch = last_infox["epoch"] + 1
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last_checkpoint_path = last_infox["last_checkpoint"]
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if not last_checkpoint_path.exists():
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logger.log(
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"Does not find {:}, try another path".format(last_checkpoint_path)
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)
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last_checkpoint_path = (
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last_info.parent
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/ last_checkpoint_path.parent.name
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/ last_checkpoint_path.name
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)
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checkpoint = torch.load(last_checkpoint_path)
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base_model.load_state_dict(checkpoint["base-model"])
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scheduler.load_state_dict(checkpoint["scheduler"])
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optimizer.load_state_dict(checkpoint["optimizer"])
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valid_accuracies = checkpoint["valid_accuracies"]
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max_bytes = checkpoint["max_bytes"]
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
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last_info, start_epoch
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)
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)
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elif args.resume is not None:
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assert Path(args.resume).exists(), "Can not find the resume file : {:}".format(
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args.resume
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)
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checkpoint = torch.load(args.resume)
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start_epoch = checkpoint["epoch"] + 1
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base_model.load_state_dict(checkpoint["base-model"])
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scheduler.load_state_dict(checkpoint["scheduler"])
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optimizer.load_state_dict(checkpoint["optimizer"])
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valid_accuracies = checkpoint["valid_accuracies"]
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max_bytes = checkpoint["max_bytes"]
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logger.log(
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"=> loading checkpoint from '{:}' start with {:}-th epoch.".format(
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args.resume, start_epoch
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)
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)
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elif args.init_model is not None:
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assert Path(
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args.init_model
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).exists(), "Can not find the initialization file : {:}".format(args.init_model)
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checkpoint = torch.load(args.init_model)
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base_model.load_state_dict(checkpoint["base-model"])
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start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
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logger.log("=> initialize the model from {:}".format(args.init_model))
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else:
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logger.log("=> do not find the last-info file : {:}".format(last_info))
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start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
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train_func, valid_func = get_procedures(args.procedure)
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total_epoch = optim_config.epochs + optim_config.warmup
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# Main Training and Evaluation Loop
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start_time = time.time()
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epoch_time = AverageMeter()
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for epoch in range(start_epoch, total_epoch):
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scheduler.update(epoch, 0.0)
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need_time = "Time Left: {:}".format(
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convert_secs2time(epoch_time.avg * (total_epoch - epoch), True)
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)
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epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
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LRs = scheduler.get_lr()
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find_best = False
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# set-up drop-out ratio
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if hasattr(base_model, "update_drop_path"):
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base_model.update_drop_path(
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model_config.drop_path_prob * epoch / total_epoch
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)
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logger.log(
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"\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}".format(
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time_string(), epoch_str, need_time, min(LRs), max(LRs), scheduler
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)
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)
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# train for one epoch
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train_loss, train_acc1, train_acc5 = train_func(
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train_loader,
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network,
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criterion,
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scheduler,
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optimizer,
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optim_config,
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epoch_str,
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args.print_freq,
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logger,
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)
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# log the results
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logger.log(
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"***{:s}*** TRAIN [{:}] loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}".format(
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time_string(), epoch_str, train_loss, train_acc1, train_acc5
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)
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)
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# evaluate the performance
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if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
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logger.log("-" * 150)
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valid_loss, valid_acc1, valid_acc5 = valid_func(
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valid_loader,
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network,
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criterion,
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optim_config,
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epoch_str,
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args.print_freq_eval,
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logger,
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)
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valid_accuracies[epoch] = valid_acc1
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logger.log(
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"***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}".format(
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time_string(),
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epoch_str,
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valid_loss,
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valid_acc1,
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valid_acc5,
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valid_accuracies["best"],
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100 - valid_accuracies["best"],
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)
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)
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if valid_acc1 > valid_accuracies["best"]:
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valid_accuracies["best"] = valid_acc1
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find_best = True
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logger.log(
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"Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.".format(
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epoch,
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valid_acc1,
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valid_acc5,
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100 - valid_acc1,
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100 - valid_acc5,
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model_best_path,
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)
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)
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num_bytes = (
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torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
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)
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logger.log(
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"[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
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next(network.parameters()).device,
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int(num_bytes),
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num_bytes / 1e3,
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num_bytes / 1e6,
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num_bytes / 1e9,
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)
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)
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max_bytes[epoch] = num_bytes
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if epoch % 10 == 0:
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torch.cuda.empty_cache()
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# save checkpoint
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save_path = save_checkpoint(
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{
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"epoch": epoch,
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"args": deepcopy(args),
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"max_bytes": deepcopy(max_bytes),
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"FLOP": flop,
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"PARAM": param,
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"valid_accuracies": deepcopy(valid_accuracies),
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"model-config": model_config._asdict(),
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"optim-config": optim_config._asdict(),
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"base-model": base_model.state_dict(),
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"scheduler": scheduler.state_dict(),
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"optimizer": optimizer.state_dict(),
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},
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model_base_path,
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logger,
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)
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if find_best:
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copy_checkpoint(model_base_path, model_best_path, logger)
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last_info = save_checkpoint(
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{
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"epoch": epoch,
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"args": deepcopy(args),
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"last_checkpoint": save_path,
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},
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logger.path("info"),
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logger,
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)
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# measure elapsed time
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epoch_time.update(time.time() - start_time)
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start_time = time.time()
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logger.log("\n" + "-" * 200)
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logger.log(
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"Finish training/validation in {:} with Max-GPU-Memory of {:.2f} MB, and save final checkpoint into {:}".format(
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convert_secs2time(epoch_time.sum, True),
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max(v for k, v in max_bytes.items()) / 1e6,
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logger.path("info"),
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)
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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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args = obtain_args()
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main(args)
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277
notebooks/spaces/test-transformer-encoder.ipynb
Normal file
277
notebooks/spaces/test-transformer-encoder.ipynb
Normal file
@@ -0,0 +1,277 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "3f754c96",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from xautodl import spaces\n",
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"from xautodl.xlayers import super_core\n",
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"\n",
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"def _create_stel(input_dim, output_dim, order):\n",
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" return super_core.SuperSequential(\n",
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" super_core.SuperLinear(input_dim, output_dim),\n",
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" super_core.SuperTransformerEncoderLayer(\n",
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" output_dim,\n",
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" num_heads=spaces.Categorical(2, 4, 6),\n",
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" mlp_hidden_multiplier=spaces.Categorical(1, 2, 4),\n",
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" order=order,\n",
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" ),\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "81d42f4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"batch, seq_dim, input_dim = 1, 4, 6\n",
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"order = super_core.LayerOrder.PreNorm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "8056b37c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"SuperSequential(\n",
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" (0): SuperSequential(\n",
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" (0): SuperLinear(in_features=6, out_features=Categorical(candidates=[12, 24, 36], default_index=None), bias=True)\n",
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" (1): SuperTransformerEncoderLayer(\n",
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" (norm1): SuperLayerNorm1D(shape=Categorical(candidates=[12, 24, 36], default_index=None), eps=1e-06, elementwise_affine=True)\n",
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" (mha): SuperSelfAttention(\n",
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" input_dim=Categorical(candidates=[12, 24, 36], default_index=None), proj_dim=Categorical(candidates=[12, 24, 36], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
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" (q_fc): SuperLinear(in_features=Categorical(candidates=[12, 24, 36], default_index=None), out_features=Categorical(candidates=[12, 24, 36], default_index=None), bias=False)\n",
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" (k_fc): SuperLinear(in_features=Categorical(candidates=[12, 24, 36], default_index=None), out_features=Categorical(candidates=[12, 24, 36], default_index=None), bias=False)\n",
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" (v_fc): SuperLinear(in_features=Categorical(candidates=[12, 24, 36], default_index=None), out_features=Categorical(candidates=[12, 24, 36], default_index=None), bias=False)\n",
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||||||
|
" (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
|
||||||
|
" )\n",
|
||||||
|
" (drop): Dropout(p=0.0, inplace=False)\n",
|
||||||
|
" (norm2): SuperLayerNorm1D(shape=Categorical(candidates=[12, 24, 36], default_index=None), eps=1e-06, elementwise_affine=True)\n",
|
||||||
|
" (mlp): SuperMLPv2(\n",
|
||||||
|
" in_features=Categorical(candidates=[12, 24, 36], default_index=None), hidden_multiplier=Categorical(candidates=[1, 2, 4], default_index=None), out_features=Categorical(candidates=[12, 24, 36], default_index=None), drop=None, fc1 -> act -> drop -> fc2 -> drop,\n",
|
||||||
|
" (_params): ParameterDict(\n",
|
||||||
|
" (fc1_super_weight): Parameter containing: [torch.FloatTensor of size 144x36]\n",
|
||||||
|
" (fc1_super_bias): Parameter containing: [torch.FloatTensor of size 144]\n",
|
||||||
|
" (fc2_super_weight): Parameter containing: [torch.FloatTensor of size 36x144]\n",
|
||||||
|
" (fc2_super_bias): Parameter containing: [torch.FloatTensor of size 36]\n",
|
||||||
|
" )\n",
|
||||||
|
" (act): GELU()\n",
|
||||||
|
" (drop): Dropout(p=0.0, inplace=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
" (1): SuperSequential(\n",
|
||||||
|
" (0): SuperLinear(in_features=Categorical(candidates=[12, 24, 36], default_index=None), out_features=Categorical(candidates=[24, 36, 48], default_index=None), bias=True)\n",
|
||||||
|
" (1): SuperTransformerEncoderLayer(\n",
|
||||||
|
" (norm1): SuperLayerNorm1D(shape=Categorical(candidates=[24, 36, 48], default_index=None), eps=1e-06, elementwise_affine=True)\n",
|
||||||
|
" (mha): SuperSelfAttention(\n",
|
||||||
|
" input_dim=Categorical(candidates=[24, 36, 48], default_index=None), proj_dim=Categorical(candidates=[24, 36, 48], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
|
||||||
|
" (q_fc): SuperLinear(in_features=Categorical(candidates=[24, 36, 48], default_index=None), out_features=Categorical(candidates=[24, 36, 48], default_index=None), bias=False)\n",
|
||||||
|
" (k_fc): SuperLinear(in_features=Categorical(candidates=[24, 36, 48], default_index=None), out_features=Categorical(candidates=[24, 36, 48], default_index=None), bias=False)\n",
|
||||||
|
" (v_fc): SuperLinear(in_features=Categorical(candidates=[24, 36, 48], default_index=None), out_features=Categorical(candidates=[24, 36, 48], default_index=None), bias=False)\n",
|
||||||
|
" (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
|
||||||
|
" )\n",
|
||||||
|
" (drop): Dropout(p=0.0, inplace=False)\n",
|
||||||
|
" (norm2): SuperLayerNorm1D(shape=Categorical(candidates=[24, 36, 48], default_index=None), eps=1e-06, elementwise_affine=True)\n",
|
||||||
|
" (mlp): SuperMLPv2(\n",
|
||||||
|
" in_features=Categorical(candidates=[24, 36, 48], default_index=None), hidden_multiplier=Categorical(candidates=[1, 2, 4], default_index=None), out_features=Categorical(candidates=[24, 36, 48], default_index=None), drop=None, fc1 -> act -> drop -> fc2 -> drop,\n",
|
||||||
|
" (_params): ParameterDict(\n",
|
||||||
|
" (fc1_super_weight): Parameter containing: [torch.FloatTensor of size 192x48]\n",
|
||||||
|
" (fc1_super_bias): Parameter containing: [torch.FloatTensor of size 192]\n",
|
||||||
|
" (fc2_super_weight): Parameter containing: [torch.FloatTensor of size 48x192]\n",
|
||||||
|
" (fc2_super_bias): Parameter containing: [torch.FloatTensor of size 48]\n",
|
||||||
|
" )\n",
|
||||||
|
" (act): GELU()\n",
|
||||||
|
" (drop): Dropout(p=0.0, inplace=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
" (2): SuperSequential(\n",
|
||||||
|
" (0): SuperLinear(in_features=Categorical(candidates=[24, 36, 48], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=True)\n",
|
||||||
|
" (1): SuperTransformerEncoderLayer(\n",
|
||||||
|
" (norm1): SuperLayerNorm1D(shape=Categorical(candidates=[36, 72, 100], default_index=None), eps=1e-06, elementwise_affine=True)\n",
|
||||||
|
" (mha): SuperSelfAttention(\n",
|
||||||
|
" input_dim=Categorical(candidates=[36, 72, 100], default_index=None), proj_dim=Categorical(candidates=[36, 72, 100], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
|
||||||
|
" (q_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (k_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (v_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
|
||||||
|
" )\n",
|
||||||
|
" (drop): Dropout(p=0.0, inplace=False)\n",
|
||||||
|
" (norm2): SuperLayerNorm1D(shape=Categorical(candidates=[36, 72, 100], default_index=None), eps=1e-06, elementwise_affine=True)\n",
|
||||||
|
" (mlp): SuperMLPv2(\n",
|
||||||
|
" in_features=Categorical(candidates=[36, 72, 100], default_index=None), hidden_multiplier=Categorical(candidates=[1, 2, 4], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), drop=None, fc1 -> act -> drop -> fc2 -> drop,\n",
|
||||||
|
" (_params): ParameterDict(\n",
|
||||||
|
" (fc1_super_weight): Parameter containing: [torch.FloatTensor of size 400x100]\n",
|
||||||
|
" (fc1_super_bias): Parameter containing: [torch.FloatTensor of size 400]\n",
|
||||||
|
" (fc2_super_weight): Parameter containing: [torch.FloatTensor of size 100x400]\n",
|
||||||
|
" (fc2_super_bias): Parameter containing: [torch.FloatTensor of size 100]\n",
|
||||||
|
" )\n",
|
||||||
|
" (act): GELU()\n",
|
||||||
|
" (drop): Dropout(p=0.0, inplace=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
")\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"out1_dim = spaces.Categorical(12, 24, 36)\n",
|
||||||
|
"out2_dim = spaces.Categorical(24, 36, 48)\n",
|
||||||
|
"out3_dim = spaces.Categorical(36, 72, 100)\n",
|
||||||
|
"layer1 = _create_stel(input_dim, out1_dim, order)\n",
|
||||||
|
"layer2 = _create_stel(out1_dim, out2_dim, order)\n",
|
||||||
|
"layer3 = _create_stel(out2_dim, out3_dim, order)\n",
|
||||||
|
"model = super_core.SuperSequential(layer1, layer2, layer3)\n",
|
||||||
|
"print(model)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "4fd53a7c",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"> \u001b[0;32m/Users/xuanyidong/anaconda3/lib/python3.8/site-packages/xautodl-0.9.9-py3.8.egg/xautodl/xlayers/super_transformer.py\u001b[0m(116)\u001b[0;36mforward_raw\u001b[0;34m()\u001b[0m\n",
|
||||||
|
"\u001b[0;32m 114 \u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0m\u001b[0;32m 115 \u001b[0;31m \u001b[0;31m# feed-forward layer -- MLP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0m\u001b[0;32m--> 116 \u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0m\u001b[0;32m 117 \u001b[0;31m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0m\u001b[0;32m 118 \u001b[0;31m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_order\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mLayerOrder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPostNorm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0m\n",
|
||||||
|
"ipdb> print(self)\n",
|
||||||
|
"SuperTransformerEncoderLayer(\n",
|
||||||
|
" (norm1): SuperLayerNorm1D(shape=Categorical(candidates=[36, 72, 100], default_index=None), eps=1e-06, elementwise_affine=True)\n",
|
||||||
|
" (mha): SuperSelfAttention(\n",
|
||||||
|
" input_dim=Categorical(candidates=[36, 72, 100], default_index=None), proj_dim=Categorical(candidates=[36, 72, 100], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
|
||||||
|
" (q_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (k_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (v_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
|
||||||
|
" )\n",
|
||||||
|
" (drop): Dropout(p=0.0, inplace=False)\n",
|
||||||
|
" (norm2): SuperLayerNorm1D(shape=Categorical(candidates=[36, 72, 100], default_index=None), eps=1e-06, elementwise_affine=True)\n",
|
||||||
|
" (mlp): SuperMLPv2(\n",
|
||||||
|
" in_features=Categorical(candidates=[36, 72, 100], default_index=None), hidden_multiplier=Categorical(candidates=[1, 2, 4], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), drop=None, fc1 -> act -> drop -> fc2 -> drop,\n",
|
||||||
|
" (_params): ParameterDict(\n",
|
||||||
|
" (fc1_super_weight): Parameter containing: [torch.FloatTensor of size 400x100]\n",
|
||||||
|
" (fc1_super_bias): Parameter containing: [torch.FloatTensor of size 400]\n",
|
||||||
|
" (fc2_super_weight): Parameter containing: [torch.FloatTensor of size 100x400]\n",
|
||||||
|
" (fc2_super_bias): Parameter containing: [torch.FloatTensor of size 100]\n",
|
||||||
|
" )\n",
|
||||||
|
" (act): GELU()\n",
|
||||||
|
" (drop): Dropout(p=0.0, inplace=False)\n",
|
||||||
|
" )\n",
|
||||||
|
")\n",
|
||||||
|
"ipdb> print(inputs.shape)\n",
|
||||||
|
"torch.Size([1, 4, 100])\n",
|
||||||
|
"ipdb> print(x.shape)\n",
|
||||||
|
"torch.Size([1, 4, 96])\n",
|
||||||
|
"ipdb> print(self.mha)\n",
|
||||||
|
"SuperSelfAttention(\n",
|
||||||
|
" input_dim=Categorical(candidates=[36, 72, 100], default_index=None), proj_dim=Categorical(candidates=[36, 72, 100], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
|
||||||
|
" (q_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (k_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (v_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
|
||||||
|
")\n",
|
||||||
|
"ipdb> print(self.mha.candidate)\n",
|
||||||
|
"*** AttributeError: 'SuperSelfAttention' object has no attribute 'candidate'\n",
|
||||||
|
"ipdb> print(self.mha.abstract_candidate)\n",
|
||||||
|
"*** AttributeError: 'SuperSelfAttention' object has no attribute 'abstract_candidate'\n",
|
||||||
|
"ipdb> print(self.mha._abstract_child)\n",
|
||||||
|
"None\n",
|
||||||
|
"ipdb> print(self.abstract_child)\n",
|
||||||
|
"None\n",
|
||||||
|
"ipdb> print(self.abstract_child.abstract_child)\n",
|
||||||
|
"*** AttributeError: 'NoneType' object has no attribute 'abstract_child'\n",
|
||||||
|
"ipdb> print(self.mha)\n",
|
||||||
|
"SuperSelfAttention(\n",
|
||||||
|
" input_dim=Categorical(candidates=[36, 72, 100], default_index=None), proj_dim=Categorical(candidates=[36, 72, 100], default_index=None), num_heads=Categorical(candidates=[2, 4, 6], default_index=None), mask=False, infinity=1000000000.0\n",
|
||||||
|
" (q_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (k_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (v_fc): SuperLinear(in_features=Categorical(candidates=[36, 72, 100], default_index=None), out_features=Categorical(candidates=[36, 72, 100], default_index=None), bias=False)\n",
|
||||||
|
" (attn_drop): SuperDrop(p=0.0, dims=[-1, -1, -1, -1], recover=True)\n",
|
||||||
|
")\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"inputs = torch.rand(batch, seq_dim, input_dim)\n",
|
||||||
|
"outputs = model(inputs)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "05332b98",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"abstract_space = model.abstract_search_space\n",
|
||||||
|
"abstract_space.clean_last()\n",
|
||||||
|
"abstract_child = abstract_space.random(reuse_last=True)\n",
|
||||||
|
"# print(\"The abstract child program is:\\n{:}\".format(abstract_child))\n",
|
||||||
|
"model.enable_candidate()\n",
|
||||||
|
"model.set_super_run_type(super_core.SuperRunMode.Candidate)\n",
|
||||||
|
"model.apply_candidate(abstract_child)\n",
|
||||||
|
"outputs = model(inputs)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "3289f938",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"print(outputs.shape)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "36951cdf",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.8.8"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
@@ -38,12 +38,15 @@ class SuperSelfAttention(SuperModule):
|
|||||||
self._use_mask = use_mask
|
self._use_mask = use_mask
|
||||||
self._infinity = 1e9
|
self._infinity = 1e9
|
||||||
|
|
||||||
mul_head_dim = (input_dim // num_heads) * num_heads
|
mul_head_dim = (
|
||||||
self.q_fc = SuperLinear(input_dim, mul_head_dim, bias=qkv_bias)
|
spaces.get_max(input_dim) // spaces.get_min(num_heads)
|
||||||
self.k_fc = SuperLinear(input_dim, mul_head_dim, bias=qkv_bias)
|
) * spaces.get_min(num_heads)
|
||||||
self.v_fc = SuperLinear(input_dim, mul_head_dim, bias=qkv_bias)
|
assert mul_head_dim == spaces.get_max(input_dim)
|
||||||
|
self.q_fc = SuperLinear(input_dim, input_dim, bias=qkv_bias)
|
||||||
|
self.k_fc = SuperLinear(input_dim, input_dim, bias=qkv_bias)
|
||||||
|
self.v_fc = SuperLinear(input_dim, input_dim, bias=qkv_bias)
|
||||||
|
|
||||||
self.attn_drop = SuperDrop(attn_drop, [-1, -1, -1, -1], recover=True)
|
self.attn_drop = SuperDrop(attn_drop or 0.0, [-1, -1, -1, -1], recover=True)
|
||||||
if proj_dim is None:
|
if proj_dim is None:
|
||||||
self.proj = SuperLinear(input_dim, proj_dim)
|
self.proj = SuperLinear(input_dim, proj_dim)
|
||||||
self.proj_drop = SuperDropout(proj_drop or 0.0)
|
self.proj_drop = SuperDropout(proj_drop or 0.0)
|
||||||
@@ -127,7 +130,18 @@ class SuperSelfAttention(SuperModule):
|
|||||||
attn_v1 = attn_v1.softmax(dim=-1) # B * #head * N * N
|
attn_v1 = attn_v1.softmax(dim=-1) # B * #head * N * N
|
||||||
attn_v1 = self.attn_drop(attn_v1)
|
attn_v1 = self.attn_drop(attn_v1)
|
||||||
feats_v1 = (attn_v1 @ v_v1).permute(0, 2, 1, 3).reshape(B, N, -1)
|
feats_v1 = (attn_v1 @ v_v1).permute(0, 2, 1, 3).reshape(B, N, -1)
|
||||||
return feats_v1
|
if C == head_dim * num_head:
|
||||||
|
feats = feats_v1
|
||||||
|
else: # The channels can not be divided by num_head, the remainder forms an additional head
|
||||||
|
q_v2 = q[:, :, num_head * head_dim :]
|
||||||
|
k_v2 = k[:, :, num_head * head_dim :]
|
||||||
|
v_v2 = v[:, :, num_head * head_dim :]
|
||||||
|
attn_v2 = (q_v2 @ k_v2.transpose(-2, -1)) * math.sqrt(q_v2.shape[-1])
|
||||||
|
attn_v2 = attn_v2.softmax(dim=-1)
|
||||||
|
attn_v2 = self.attn_drop(attn_v2)
|
||||||
|
feats_v2 = attn_v2 @ v_v2
|
||||||
|
feats = torch.cat([feats_v1, feats_v2], dim=-1)
|
||||||
|
return feats
|
||||||
|
|
||||||
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
|
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
|
||||||
# check the num_heads:
|
# check the num_heads:
|
||||||
|
|||||||
@@ -5,3 +5,6 @@
|
|||||||
#####################################################
|
#####################################################
|
||||||
|
|
||||||
from .transformers import get_transformer
|
from .transformers import get_transformer
|
||||||
|
|
||||||
|
def obtain_model(config):
|
||||||
|
raise NotImplementedError
|
||||||
|
|||||||
Reference in New Issue
Block a user