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Fix bugs in ViT

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D-X-Y 2021-06-09 23:08:21 +08:00
parent d4546cfe3f
commit aef5c7579b
4 changed files with 594 additions and 6 deletions
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exps/basic/xmain.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
#####################################################
import sys, time, torch, random, argparse
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from copy import deepcopy
from pathlib import Path
from xautodl.datasets import get_datasets
from xautodl.config_utils import load_config, obtain_basic_args as obtain_args
from xautodl.procedures import (
prepare_seed,
prepare_logger,
save_checkpoint,
copy_checkpoint,
)
from xautodl.procedures import get_optim_scheduler, get_procedures
from xautodl.models import obtain_model
from xautodl.xmodels import obtain_model as obtain_xmodel
from xautodl.nas_infer_model import obtain_nas_infer_model
from xautodl.utils import get_model_infos
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
def main(args):
assert torch.cuda.is_available(), "CUDA is not available."
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
# torch.backends.cudnn.deterministic = True
# torch.set_num_threads(args.workers)
prepare_seed(args.rand_seed)
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(
args.dataset, args.data_path, args.cutout_length
)
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
)
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
# get configures
model_config = load_config(args.model_config, {"class_num": class_num}, logger)
optim_config = load_config(args.optim_config, {"class_num": class_num}, logger)
if args.model_source == "normal":
base_model = obtain_model(model_config)
elif args.model_source == "nas":
base_model = obtain_nas_infer_model(model_config, args.extra_model_path)
elif args.model_source == "autodl-searched":
base_model = obtain_model(model_config, args.extra_model_path)
elif args.model_source in ("x", "xmodel"):
base_model = obtain_xmodel(model_config)
else:
raise ValueError("invalid model-source : {:}".format(args.model_source))
flop, param = get_model_infos(base_model, xshape)
logger.log("model ====>>>>:\n{:}".format(base_model))
logger.log("model information : {:}".format(base_model.get_message()))
logger.log("-" * 50)
logger.log(
"Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(
param, flop, flop / 1e3
)
)
logger.log("-" * 50)
logger.log("train_data : {:}".format(train_data))
logger.log("valid_data : {:}".format(valid_data))
optimizer, scheduler, criterion = get_optim_scheduler(
base_model.parameters(), optim_config
)
logger.log("optimizer : {:}".format(optimizer))
logger.log("scheduler : {:}".format(scheduler))
logger.log("criterion : {:}".format(criterion))
last_info, model_base_path, model_best_path = (
logger.path("info"),
logger.path("model"),
logger.path("best"),
)
network, criterion = torch.nn.DataParallel(base_model).cuda(), criterion.cuda()
if last_info.exists(): # automatically resume from previous checkpoint
logger.log(
"=> loading checkpoint of the last-info '{:}' start".format(last_info)
)
last_infox = torch.load(last_info)
start_epoch = last_infox["epoch"] + 1
last_checkpoint_path = last_infox["last_checkpoint"]
if not last_checkpoint_path.exists():
logger.log(
"Does not find {:}, try another path".format(last_checkpoint_path)
)
last_checkpoint_path = (
last_info.parent
/ last_checkpoint_path.parent.name
/ last_checkpoint_path.name
)
checkpoint = torch.load(last_checkpoint_path)
base_model.load_state_dict(checkpoint["base-model"])
scheduler.load_state_dict(checkpoint["scheduler"])
optimizer.load_state_dict(checkpoint["optimizer"])
valid_accuracies = checkpoint["valid_accuracies"]
max_bytes = checkpoint["max_bytes"]
logger.log(
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
last_info, start_epoch
)
)
elif args.resume is not None:
assert Path(args.resume).exists(), "Can not find the resume file : {:}".format(
args.resume
)
checkpoint = torch.load(args.resume)
start_epoch = checkpoint["epoch"] + 1
base_model.load_state_dict(checkpoint["base-model"])
scheduler.load_state_dict(checkpoint["scheduler"])
optimizer.load_state_dict(checkpoint["optimizer"])
valid_accuracies = checkpoint["valid_accuracies"]
max_bytes = checkpoint["max_bytes"]
logger.log(
"=> loading checkpoint from '{:}' start with {:}-th epoch.".format(
args.resume, start_epoch
)
)
elif args.init_model is not None:
assert Path(
args.init_model
).exists(), "Can not find the initialization file : {:}".format(args.init_model)
checkpoint = torch.load(args.init_model)
base_model.load_state_dict(checkpoint["base-model"])
start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
logger.log("=> initialize the model from {:}".format(args.init_model))
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
train_func, valid_func = get_procedures(args.procedure)
total_epoch = optim_config.epochs + optim_config.warmup
# Main Training and Evaluation Loop
start_time = time.time()
epoch_time = AverageMeter()
for epoch in range(start_epoch, total_epoch):
scheduler.update(epoch, 0.0)
need_time = "Time Left: {:}".format(
convert_secs2time(epoch_time.avg * (total_epoch - epoch), True)
)
epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
LRs = scheduler.get_lr()
find_best = False
# set-up drop-out ratio
if hasattr(base_model, "update_drop_path"):
base_model.update_drop_path(
model_config.drop_path_prob * epoch / total_epoch
)
logger.log(
"\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}".format(
time_string(), epoch_str, need_time, min(LRs), max(LRs), scheduler
)
)
# train for one epoch
train_loss, train_acc1, train_acc5 = train_func(
train_loader,
network,
criterion,
scheduler,
optimizer,
optim_config,
epoch_str,
args.print_freq,
logger,
)
# log the results
logger.log(
"***{:s}*** TRAIN [{:}] loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}".format(
time_string(), epoch_str, train_loss, train_acc1, train_acc5
)
)
# evaluate the performance
if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
logger.log("-" * 150)
valid_loss, valid_acc1, valid_acc5 = valid_func(
valid_loader,
network,
criterion,
optim_config,
epoch_str,
args.print_freq_eval,
logger,
)
valid_accuracies[epoch] = valid_acc1
logger.log(
"***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}".format(
time_string(),
epoch_str,
valid_loss,
valid_acc1,
valid_acc5,
valid_accuracies["best"],
100 - valid_accuracies["best"],
)
)
if valid_acc1 > valid_accuracies["best"]:
valid_accuracies["best"] = valid_acc1
find_best = True
logger.log(
"Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.".format(
epoch,
valid_acc1,
valid_acc5,
100 - valid_acc1,
100 - valid_acc5,
model_best_path,
)
)
num_bytes = (
torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
)
logger.log(
"[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
next(network.parameters()).device,
int(num_bytes),
num_bytes / 1e3,
num_bytes / 1e6,
num_bytes / 1e9,
)
)
max_bytes[epoch] = num_bytes
if epoch % 10 == 0:
torch.cuda.empty_cache()
# save checkpoint
save_path = save_checkpoint(
{
"epoch": epoch,
"args": deepcopy(args),
"max_bytes": deepcopy(max_bytes),
"FLOP": flop,
"PARAM": param,
"valid_accuracies": deepcopy(valid_accuracies),
"model-config": model_config._asdict(),
"optim-config": optim_config._asdict(),
"base-model": base_model.state_dict(),
"scheduler": scheduler.state_dict(),
"optimizer": optimizer.state_dict(),
},
model_base_path,
logger,
)
if find_best:
copy_checkpoint(model_base_path, model_best_path, logger)
last_info = save_checkpoint(
{
"epoch": epoch,
"args": deepcopy(args),
"last_checkpoint": save_path,
},
logger.path("info"),
logger,
)
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
logger.log("\n" + "-" * 200)
logger.log(
"Finish training/validation in {:} with Max-GPU-Memory of {:.2f} MB, and save final checkpoint into {:}".format(
convert_secs2time(epoch_time.sum, True),
max(v for k, v in max_bytes.items()) / 1e6,
logger.path("info"),
)
)
logger.log("-" * 200 + "\n")
logger.close()
if __name__ == "__main__":
args = obtain_args()
main(args)

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3f754c96",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from xautodl import spaces\n",
"from xautodl.xlayers import super_core\n",
"\n",
"def _create_stel(input_dim, output_dim, order):\n",
" return super_core.SuperSequential(\n",
" super_core.SuperLinear(input_dim, output_dim),\n",
" super_core.SuperTransformerEncoderLayer(\n",
" output_dim,\n",
" num_heads=spaces.Categorical(2, 4, 6),\n",
" mlp_hidden_multiplier=spaces.Categorical(1, 2, 4),\n",
" order=order,\n",
" ),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "81d42f4b",
"metadata": {},
"outputs": [],
"source": [
"batch, seq_dim, input_dim = 1, 4, 6\n",
"order = super_core.LayerOrder.PreNorm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8056b37c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SuperSequential(\n",
" (0): SuperSequential(\n",
" (0): SuperLinear(in_features=6, out_features=Categorical(candidates=[12, 24, 36], default_index=None), bias=True)\n",
" (1): SuperTransformerEncoderLayer(\n",
" (norm1): SuperLayerNorm1D(shape=Categorical(candidates=[12, 24, 36], default_index=None), eps=1e-06, elementwise_affine=True)\n",
" (mha): SuperSelfAttention(\n",
" 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",
" (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",
" (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",
" (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",
" (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
}

26
xautodl/xlayers/super_attention.py
View File

@ -38,12 +38,15 @@ class SuperSelfAttention(SuperModule):
self._use_mask = use_mask
self._infinity = 1e9
mul_head_dim = (input_dim // num_heads) * num_heads
self.q_fc = SuperLinear(input_dim, mul_head_dim, bias=qkv_bias)
self.k_fc = SuperLinear(input_dim, mul_head_dim, bias=qkv_bias)
self.v_fc = SuperLinear(input_dim, mul_head_dim, bias=qkv_bias)
mul_head_dim = (
spaces.get_max(input_dim) // spaces.get_min(num_heads)
) * spaces.get_min(num_heads)
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:
self.proj = SuperLinear(input_dim, proj_dim)
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 = self.attn_drop(attn_v1)
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:
# check the num_heads:

3
xautodl/xmodels/__init__.py
View File

@ -5,3 +5,6 @@
#####################################################
from .transformers import get_transformer
def obtain_model(config):
raise NotImplementedError