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Fix small bugs

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This commit is contained in:
D-X-Y
2021-08-14 16:01:07 -07:00
parent 58733c18be
commit d04edcd211
12 changed files with 95 additions and 18 deletions
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2
.gitmodules vendored
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@@ -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

2
.latent-data/qlib

Submodule .latent-data/qlib updated: 2d4f0e80f9...6608a40965

10
configs/archs/NAS-CIFAR-DARTS.config Normal file
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@@ -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]
}

2
configs/qlib/workflow_config_transformer_basic_Alpha360.yaml
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@@ -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

3
docs/BASELINE.md
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@@ -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

4
docs/CVPR-2019-GDAS.md
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@@ -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

59
exps/trading/workflow_tt.py
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@@ -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):

1
scripts/black.sh
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@@ -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

6
setup.py
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@@ -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*")

3
xautodl/__init__.py
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@@ -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]

9
xautodl/trade_models/transformers.py
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@@ -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)),
num_heads=_get_mul_specs((1, 2, 4, 8), _default_max_depth),
mlp_hidden_multipliers=_get_mul_specs((0.5, 1, 2, 4, 8), _default_max_depth),
embed_dim=32,
# embed_dim=spaces.Categorical(*_get_list_mul(8, 16)),
num_heads=[4] * _default_max_depth,
mlp_hidden_multipliers=[4] * _default_max_depth,
qkv_bias=True,
pos_drop=0.0,
other_drop=0.0,

12
xautodl/utils/flop_benchmark.py
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@@ -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