Refine Transformer

This commit is contained in:
D-X-Y 2021-07-04 11:59:06 +00:00
parent 9136f33684
commit 11f313288a
10 changed files with 160 additions and 28 deletions

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@ -4,7 +4,7 @@ import torch.nn.functional as F
from xautodl.xlayers import super_core
from xautodl.xlayers import trunc_normal_
from xautodl.models.xcore import get_model
from xautodl.xmodels.xcore import get_model
class MetaModelV1(super_core.SuperModule):

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@ -8,6 +8,9 @@
import os, sys, time, torch
import pickle
import tempfile
from pathlib import Path
root_dir = (Path(__file__).parent / ".." / "..").resolve()
from xautodl.trade_models.quant_transformer import QuantTransformer
@ -17,7 +20,7 @@ def test_create():
if not torch.cuda.is_available():
return
quant_model = QuantTransformer(GPU=0)
temp_dir = lib_dir / ".." / "tests" / ".pytest_cache"
temp_dir = root_dir / "tests" / ".pytest_cache"
temp_dir.mkdir(parents=True, exist_ok=True)
temp_file = temp_dir / "quant-model.pkl"
with temp_file.open("wb") as f:
@ -30,7 +33,7 @@ def test_create():
def test_load():
temp_file = lib_dir / ".." / "tests" / ".pytest_cache" / "quant-model.pkl"
temp_file = root_dir / "tests" / ".pytest_cache" / "quant-model.pkl"
with temp_file.open("rb") as f:
model = pickle.load(f)
print(model.model)

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@ -21,10 +21,10 @@ import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as th_data
from log_utils import AverageMeter
from utils import count_parameters
from xautodl.xmisc import AverageMeter
from xautodl.xmisc import count_parameters
from xlayers import super_core
from xautodl.xlayers import super_core
from .transformers import DEFAULT_NET_CONFIG
from .transformers import get_transformer

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@ -13,7 +13,7 @@ import torch.nn as nn
import torch.nn.functional as F
from xautodl import spaces
from xautodl.xlayers import trunc_normal_
from xautodl.xlayers import weight_init
from xautodl.xlayers import super_core
@ -104,7 +104,7 @@ class SuperTransformer(super_core.SuperModule):
self.head = super_core.SuperSequential(
super_core.SuperLayerNorm1D(embed_dim), super_core.SuperLinear(embed_dim, 1)
)
trunc_normal_(self.cls_token, std=0.02)
weight_init.trunc_normal_(self.cls_token, std=0.02)
self.apply(self._init_weights)
@property
@ -136,11 +136,11 @@ class SuperTransformer(super_core.SuperModule):
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
weight_init.trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, super_core.SuperLinear):
trunc_normal_(m._super_weight, std=0.02)
weight_init.trunc_normal_(m._super_weight, std=0.02)
if m._super_bias is not None:
nn.init.constant_(m._super_bias, 0)
elif isinstance(m, super_core.SuperLayerNorm1D):

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@ -4,5 +4,4 @@
# This file is expected to be self-contained, expect
# for importing from spaces to include search space.
#####################################################
from .weight_init import trunc_normal_
from .super_core import *

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@ -1,8 +1,12 @@
# Borrowed from https://github.com/rwightman/pytorch-image-models
import torch
import torch.nn as nn
import math
import warnings
# setup for xlayers
from . import super_core
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
@ -64,3 +68,17 @@ def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
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)
def init_transformer(m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, super_core.SuperLinear):
trunc_normal_(m._super_weight, std=0.02)
if m._super_bias is not None:
nn.init.constant_(m._super_bias, 0)
elif isinstance(m, super_core.SuperLayerNorm1D):
nn.init.constant_(m.weight, 1.0)
nn.init.constant_(m.bias, 0)

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@ -4,4 +4,4 @@
# The models in this folder is written with xlayers #
#####################################################
from .transformers import get_transformer
from .core import *

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@ -15,7 +15,7 @@ from xautodl.xlayers.super_core import super_name2activation
def get_model(config: Dict[Text, Any], **kwargs):
model_type = config.get("model_type", "simple_mlp")
model_type = config.get("model_type", "simple_mlp").lower()
if model_type == "simple_mlp":
act_cls = super_name2activation[kwargs["act_cls"]]
norm_cls = super_name2norm[kwargs["norm_cls"]]
@ -60,6 +60,8 @@ def get_model(config: Dict[Text, Any], **kwargs):
last_dim = hidden_dim
sub_layers.append(SuperLinear(last_dim, kwargs["output_dim"]))
model = SuperSequential(*sub_layers)
elif model_type == "quant_transformer":
raise NotImplementedError
else:
raise TypeError("Unkonwn model type: {:}".format(model_type))
return model

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@ -20,20 +20,6 @@ def pair(t):
return t if isinstance(t, tuple) else (t, t)
def _init_weights(m):
if isinstance(m, nn.Linear):
weight_init.trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, xlayers.SuperLinear):
weight_init.trunc_normal_(m._super_weight, std=0.02)
if m._super_bias is not None:
nn.init.constant_(m._super_bias, 0)
elif isinstance(m, xlayers.SuperLayerNorm1D):
nn.init.constant_(m.weight, 1.0)
nn.init.constant_(m.bias, 0)
name2config = {
"vit-cifar10-p4-d4-h4-c32": dict(
type="vit",
@ -155,7 +141,7 @@ class SuperViT(xlayers.SuperModule):
)
weight_init.trunc_normal_(self.cls_token, std=0.02)
self.apply(_init_weights)
self.apply(weight_init.init_transformer)
@property
def abstract_search_space(self):

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@ -0,0 +1,124 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
#####################################################
# Vision Transformer: arxiv.org/pdf/2010.11929.pdf #
#####################################################
import copy, math
from functools import partial
from typing import Optional, Text, List
import torch
import torch.nn as nn
import torch.nn.functional as F
from xautodl import spaces
from xautodl import xlayers
from xautodl.xlayers import weight_init
class SuperQuaT(xlayers.SuperModule):
"""The super transformer for transformer."""
def __init__(
self,
image_size,
patch_size,
num_classes,
dim,
depth,
heads,
mlp_multiplier=4,
channels=3,
dropout=0.0,
att_dropout=0.0,
):
super(SuperQuaT, self).__init__()
image_height, image_width = pair(image_size)
patch_height, patch_width = pair(patch_size)
if image_height % patch_height != 0 or image_width % patch_width != 0:
raise ValueError("Image dimensions must be divisible by the patch size.")
num_patches = (image_height // patch_height) * (image_width // patch_width)
patch_dim = channels * patch_height * patch_width
self.to_patch_embedding = xlayers.SuperSequential(
xlayers.SuperReArrange(
"b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
p1=patch_height,
p2=patch_width,
),
xlayers.SuperLinear(patch_dim, dim),
)
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
self.dropout = nn.Dropout(dropout)
# build the transformer encode layers
layers = []
for ilayer in range(depth):
layers.append(
xlayers.SuperTransformerEncoderLayer(
dim,
heads,
False,
mlp_multiplier,
dropout=dropout,
att_dropout=att_dropout,
)
)
self.backbone = xlayers.SuperSequential(*layers)
self.cls_head = xlayers.SuperSequential(
xlayers.SuperLayerNorm1D(dim), xlayers.SuperLinear(dim, num_classes)
)
weight_init.trunc_normal_(self.cls_token, std=0.02)
self.apply(_init_weights)
@property
def abstract_search_space(self):
raise NotImplementedError
def apply_candidate(self, abstract_child: spaces.VirtualNode):
super(SuperQuaT, self).apply_candidate(abstract_child)
raise NotImplementedError
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
raise NotImplementedError
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
tensors = self.to_patch_embedding(input)
batch, seq, _ = tensors.shape
cls_tokens = self.cls_token.expand(batch, -1, -1)
feats = torch.cat((cls_tokens, tensors), dim=1)
feats = feats + self.pos_embedding[:, : seq + 1, :]
feats = self.dropout(feats)
feats = self.backbone(feats)
x = feats[:, 0] # the features for cls-token
return self.cls_head(x)
def get_transformer(config):
if isinstance(config, str) and config.lower() in name2config:
config = name2config[config.lower()]
if not isinstance(config, dict):
raise ValueError("Invalid Configuration: {:}".format(config))
model_type = config.get("type", "vit").lower()
if model_type == "vit":
model = SuperQuaT(
image_size=config.get("image_size"),
patch_size=config.get("patch_size"),
num_classes=config.get("num_classes"),
dim=config.get("dim"),
depth=config.get("depth"),
heads=config.get("heads"),
dropout=config.get("dropout"),
att_dropout=config.get("att_dropout"),
)
else:
raise ValueError("Unknown model type: {:}".format(model_type))
return model