Refine Transformer

exps/experimental/GeMOSA/meta_model.py

@@ -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):

tests/test_torch_gpu_bugs.py

@@ -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)

xautodl/trade_models/quant_transformer.py

@@ -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 xautodl.xmisc import AverageMeter
-from utils import count_parameters
+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
 

xautodl/trade_models/transformers.py

@@ -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):

xautodl/xlayers/__init__.py

@@ -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 *

xautodl/xlayers/weight_init.py

@@ -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)

xautodl/xmodels/__init__.py

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

xautodl/xmodels/core.py

@@ -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

xautodl/xmodels/transformers.py

@@ -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):

xautodl/xmodels/transformers_quantum.py

@@ -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