Fix small bugs

.gitmodules

@@ -1,6 +1,6 @@
 [submodule ".latent-data/qlib"]
 	path = .latent-data/qlib
-	url = git@github.com:D-X-Y/qlib.git
+	url = git@github.com:microsoft/qlib.git
 [submodule ".latent-data/NATS-Bench"]
 	path = .latent-data/NATS-Bench
 	url = git@github.com:D-X-Y/NATS-Bench.git

configs/archs/NAS-CIFAR-DARTS.config

@@ -0,0 +1,10 @@
+{
+  "arch"      : ["str",  "dxys"],
+  "genotype"  : ["str",  "DARTS"],
+  "dataset"   : ["str",  "cifar"],
+  "ichannel"  : ["int",   36],
+  "layers"    : ["int",    6],
+  "stem_multi": ["int",    3],
+  "auxiliary" : ["bool",   1],
+  "drop_path_prob": ["float", 0.2]
+}

configs/qlib/workflow_config_transformer_basic_Alpha360.yaml

@@ -47,7 +47,7 @@ task:
             net_config:
                 name: basic
                 d_feat: 6
-                embed_dim: 48
+                embed_dim: 32
                 num_heads: [4, 4, 4, 4, 4]
                 mlp_hidden_multipliers: [4, 4, 4, 4, 4]
                 qkv_bias: True

docs/BASELINE.md

@@ -32,6 +32,9 @@ Train some NAS models:
 ```
 CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10  SETN 96 -1
 CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 SETN 96 -1
+
+CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 DARTS 96 -1
+
 CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN  256 -1
 CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN1 256 -1
 CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k DARTS 256 -1

docs/CVPR-2019-GDAS.md

@@ -22,7 +22,7 @@ from utils import get_model_infos
 flop, param  = get_model_infos(net, (1,3,32,32))
 ```
 
-2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/genotypes.py).
+2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/AutoDL-Projects/blob/main/xautodl/nas_infer_model/DXYs/genotypes.py).
 
 
 ## Usage
@@ -34,7 +34,7 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10  GDAS_V1 96 -1
 CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 GDAS_V1 96 -1
 CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_V1 256 -1
 ```
-If you are interested in the configs of each NAS-searched architecture, they are defined at [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/genotypes.py).
+If you are interested in the configs of each NAS-searched architecture, they are defined at [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/main/xautodl/nas_infer_model/DXYs/genotypes.py).
 
 ### Searching on the NASNet search space
 

exps/trading/workflow_tt.py

@@ -6,6 +6,7 @@
 # - https://github.com/microsoft/qlib/blob/main/examples/workflow_by_code.py
 # python exps/trading/workflow_tt.py --gpu 1 --market csi300
 #####################################################
+import yaml
 import argparse
 
 from xautodl.procedures.q_exps import update_gpu
@@ -57,7 +58,7 @@ def main(xargs):
 
     model_config = {
         "class": "QuantTransformer",
-        "module_path": "trade_models",
+        "module_path": "xautodl.trade_models.quant_transformer",
         "kwargs": {
             "net_config": None,
             "opt_config": None,
@@ -108,6 +109,62 @@ def main(xargs):
     provider_uri = "~/.qlib/qlib_data/cn_data"
     qlib.init(provider_uri=provider_uri, region=REG_CN)
 
+    from qlib.utils import init_instance_by_config
+
+    xconfig = """
+model:
+        class: SFM
+        module_path: qlib.contrib.model.pytorch_sfm
+        kwargs:
+            d_feat: 6
+            hidden_size: 64
+            output_dim: 32
+            freq_dim: 25
+            dropout_W: 0.5
+            dropout_U: 0.5
+            n_epochs: 20
+            lr: 1e-3
+            batch_size: 1600
+            early_stop: 20
+            eval_steps: 5
+            loss: mse
+            optimizer: adam
+            GPU: 0
+"""
+    xconfig = """
+model:
+        class: TabnetModel
+        module_path: qlib.contrib.model.pytorch_tabnet
+        kwargs:
+            d_feat: 360
+            pretrain: True
+"""
+    xconfig = """
+model:
+        class: GRU
+        module_path: qlib.contrib.model.pytorch_gru
+        kwargs:
+            d_feat: 6
+            hidden_size: 64
+            num_layers: 4
+            dropout: 0.0
+            n_epochs: 200
+            lr: 0.001
+            early_stop: 20
+            batch_size: 800
+            metric: loss
+            loss: mse
+            GPU: 0
+"""
+    xconfig = yaml.safe_load(xconfig)
+    model = init_instance_by_config(xconfig["model"])
+    from xautodl.utils.flop_benchmark import count_parameters_in_MB
+
+    # print(count_parameters_in_MB(model.tabnet_model))
+    import pdb
+
+    pdb.set_trace()
+
     save_dir = "{:}-{:}".format(xargs.save_dir, xargs.market)
     dataset = init_instance_by_config(dataset_config)
     for irun in range(xargs.times):

scripts/black.sh

@@ -12,7 +12,6 @@ black ./tests/
 black ./xautodl/procedures
 black ./xautodl/datasets
 black ./xautodl/xlayers
-black ./exps/LFNA
 black ./exps/trading
 rm -rf ./xautodl.egg-info
 rm -rf ./build

setup.py

@@ -17,6 +17,8 @@
 # TODO(xuanyidong): upload it to conda
 #
 # [2021.06.01] v0.9.9
+# [2021.08.14] v1.0.0
+# 
 import os
 from setuptools import setup, find_packages
 
@@ -24,7 +26,7 @@ NAME = "xautodl"
 REQUIRES_PYTHON = ">=3.6"
 DESCRIPTION = "Automated Deep Learning Package"
 
-VERSION = "0.9.9"
+VERSION = "1.0.0"
 
 
 def read(fname="README.md"):
@@ -35,7 +37,7 @@ def read(fname="README.md"):
 
 
 # What packages are required for this module to be executed?
-REQUIRED = ["numpy>=1.16.5,<=1.19.5", "pyyaml>=5.0.0"]
+REQUIRED = ["numpy>=1.16.5,<=1.19.5", "pyyaml>=5.0.0", "fvcore"]
 
 packages = find_packages(
     exclude=("tests", "scripts", "scripts-search", "lib*", "exps*")

xautodl/__init__.py

@@ -7,5 +7,6 @@
 
 
 def version():
-    versions = ["0.9.9"]  # 2021.05.19
+    versions = ["0.9.9"]  # 2021.06.01
+    versions = ["1.0.0"]  # 2021.08.14
     return versions[-1]

xautodl/trade_models/transformers.py

@@ -42,12 +42,13 @@ def _assert_types(x, expected_types):
 
 
 DEFAULT_NET_CONFIG = None
-_default_max_depth = 5
+_default_max_depth = 6
 DefaultSearchSpace = dict(
     d_feat=6,
-    embed_dim=spaces.Categorical(*_get_list_mul(8, 16)),
+    embed_dim=32,
-    num_heads=_get_mul_specs((1, 2, 4, 8), _default_max_depth),
+    # embed_dim=spaces.Categorical(*_get_list_mul(8, 16)),
-    mlp_hidden_multipliers=_get_mul_specs((0.5, 1, 2, 4, 8), _default_max_depth),
+    num_heads=[4] * _default_max_depth,
+    mlp_hidden_multipliers=[4] * _default_max_depth,
     qkv_bias=True,
     pos_drop=0.0,
     other_drop=0.0,

xautodl/utils/flop_benchmark.py

@@ -14,20 +14,24 @@ def count_parameters(model_or_parameters, unit="mb", deprecated=False):
     if isinstance(model_or_parameters, nn.Module):
         counts = sum(np.prod(v.size()) for v in model_or_parameters.parameters())
     elif isinstance(model_or_parameters, nn.Parameter):
-        counts = models_or_parameters.numel()
+        counts = model_or_parameters.numel()
     elif isinstance(model_or_parameters, (list, tuple)):
         counts = sum(
-            count_parameters(x, None, deprecated) for x in models_or_parameters
+            count_parameters(x, None, deprecated) for x in model_or_parameters
         )
     else:
         counts = sum(np.prod(v.size()) for v in model_or_parameters)
-    if unit.lower() == "kb" or unit.lower() == "k":
+    if not isinstance(unit, str) and unit is not None:
+        raise ValueError("Unknow type of unit: {:}".format(unit))
+    elif unit is None:
+        counts = counts
+    elif unit.lower() == "kb" or unit.lower() == "k":
         counts /= 1e3 if deprecated else 2 ** 10  # changed from 1e3 to 2^10
     elif unit.lower() == "mb" or unit.lower() == "m":
         counts /= 1e6 if deprecated else 2 ** 20  # changed from 1e6 to 2^20
     elif unit.lower() == "gb" or unit.lower() == "g":
         counts /= 1e9 if deprecated else 2 ** 30  # changed from 1e9 to 2^30
-    elif unit is not None:
+    else:
         raise ValueError("Unknow unit: {:}".format(unit))
     return counts