iicd/autodl-projects
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Updates

Browse Source
This commit is contained in:
D-X-Y 2021-03-15 02:58:34 +00:00
parent 1c947f26c7
commit e169aabe77
1 changed files with 21 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

36
lib/trade_models/quant_transformer.py
View File

@ -4,8 +4,7 @@
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import os import os, math, random
import math
from collections import OrderedDict from collections import OrderedDict
import numpy as np import numpy as np
import pandas as pd import pandas as pd
@ -37,7 +36,7 @@ from qlib.data.dataset import DatasetH
from qlib.data.dataset.handler import DataHandlerLP from qlib.data.dataset.handler import DataHandlerLP
default_net_config = dict(d_feat=6, hidden_size=48, depth=5, pos_drop=0.1) default_net_config = dict(d_feat=6, embed_dim=48, depth=5, num_heads=4, mlp_ratio=4.0, qkv_bias=True, pos_drop=0.1)
default_opt_config = dict( default_opt_config = dict(
epochs=200, lr=0.001, batch_size=2000, early_stop=20, loss="mse", optimizer="adam", num_workers=4 epochs=200, lr=0.001, batch_size=2000, early_stop=20, loss="mse", optimizer="adam", num_workers=4
@ -50,7 +49,7 @@ class QuantTransformer(Model):
def __init__(self, net_config=None, opt_config=None, metric="", GPU=0, seed=None, **kwargs): def __init__(self, net_config=None, opt_config=None, metric="", GPU=0, seed=None, **kwargs):
# Set logger. # Set logger.
self.logger = get_module_logger("QuantTransformer") self.logger = get_module_logger("QuantTransformer")
self.logger.info("QuantTransformer pytorch version...") self.logger.info("QuantTransformer PyTorch version...")
# set hyper-parameters. # set hyper-parameters.
self.net_config = net_config or default_net_config self.net_config = net_config or default_net_config
@ -75,12 +74,16 @@ class QuantTransformer(Model):
) )
if self.seed is not None: if self.seed is not None:
random.seed(self.seed)
np.random.seed(self.seed) np.random.seed(self.seed)
torch.manual_seed(self.seed) torch.manual_seed(self.seed)
if self.use_gpu:
torch.cuda.manual_seed(self.seed)
torch.cuda.manual_seed_all(self.seed)
self.model = TransformerModel( self.model = TransformerModel(
d_feat=self.net_config["d_feat"], d_feat=self.net_config["d_feat"],
embed_dim=self.net_config["hidden_size"], embed_dim=self.net_config["embed_dim"],
depth=self.net_config["depth"], depth=self.net_config["depth"],
pos_drop=self.net_config["pos_drop"], pos_drop=self.net_config["pos_drop"],
) )
@ -99,7 +102,7 @@ class QuantTransformer(Model):
@property @property
def use_gpu(self): def use_gpu(self):
self.device == torch.device("cpu") return self.device != torch.device("cpu")
def loss_fn(self, pred, label): def loss_fn(self, pred, label):
mask = ~torch.isnan(label) mask = ~torch.isnan(label)
@ -176,7 +179,7 @@ class QuantTransformer(Model):
_prepare_loader(test_dataset, False), _prepare_loader(test_dataset, False),
) )
save_path = get_or_create_path(save_path) save_path = get_or_create_path(save_path, return_dir=True)
self.logger.info("Fit procedure for [{:}] with save path={:}".format(self.__class__.__name__, save_path)) self.logger.info("Fit procedure for [{:}] with save path={:}".format(self.__class__.__name__, save_path))
def _internal_test(ckp_epoch=None, results_dict=None): def _internal_test(ckp_epoch=None, results_dict=None):
@ -286,11 +289,11 @@ class QuantTransformer(Model):
class Attention(nn.Module): class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0):
super(Attention, self).__init__() super(Attention, self).__init__()
self.num_heads = num_heads self.num_heads = num_heads
head_dim = dim // num_heads head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or math.sqrt(head_dim) self.scale = qk_scale or math.sqrt(head_dim)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
@ -314,6 +317,7 @@ class Attention(nn.Module):
class Block(nn.Module): class Block(nn.Module):
def __init__( def __init__(
self, self,
dim, dim,
@ -345,6 +349,7 @@ class Block(nn.Module):
class SimpleEmbed(nn.Module): class SimpleEmbed(nn.Module):
def __init__(self, d_feat, embed_dim): def __init__(self, d_feat, embed_dim):
super(SimpleEmbed, self).__init__() super(SimpleEmbed, self).__init__()
self.d_feat = d_feat self.d_feat = d_feat
@ -361,18 +366,19 @@ class SimpleEmbed(nn.Module):
class TransformerModel(nn.Module): class TransformerModel(nn.Module):
def __init__( def __init__(
self, self,
d_feat: int, d_feat: int = 6,
embed_dim: int = 64, embed_dim: int = 64,
depth: int = 4, depth: int = 4,
num_heads: int = 4, num_heads: int = 4,
mlp_ratio: float = 4.0, mlp_ratio: float = 4.0,
qkv_bias: bool = True, qkv_bias: bool = True,
qk_scale: Optional[float] = None, qk_scale: Optional[float] = None,
pos_drop=0.0, pos_drop: float = 0.0,
mlp_drop_rate=0.0, mlp_drop_rate: float = 0.0,
attn_drop_rate=0.0, attn_drop_rate: float = 0.0,
drop_path_rate=0.0, drop_path_rate: float = 0.0,
norm_layer=None, norm_layer: Optional[nn.Module] = None,
max_seq_len: int = 65,
): ):
""" """
Args: Args:
@ -397,7 +403,7 @@ class TransformerModel(nn.Module):
self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim) self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim)
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = xlayers.PositionalEncoder(d_model=embed_dim, max_seq_len=65, dropout=pos_drop) self.pos_embed = xlayers.PositionalEncoder(d_model=embed_dim, max_seq_len=max_seq_len, dropout=pos_drop)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList( self.blocks = nn.ModuleList(