-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_DT.py
226 lines (189 loc) · 8.73 KB
/
train_DT.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
import os
import random
import warnings
from dataclasses import dataclass
import numpy as np
import torch
from transformers import DecisionTransformerConfig, DecisionTransformerModel, Trainer, TrainingArguments
from datasets import load_from_disk
@dataclass
class DecisionTransformerCityLearnDataCollator:
"""
Data collators are objects that will form a batch by using a list of dataset elements as input.
Args:
dataset ('List[dict]'):
Offline dataset to train the model with.
max_ep_len ('float'):
Length of an episode in the dataset
max_len ('float'):
Subsets of the episode we use for training
scale ('float'):
normalization of rewards/returns
"""
return_tensors: str = "pt"
state_mean: np.array = None # to store state means
state_std: np.array = None # to store state stds
p_sample: np.array = None # a distribution to take account trajectory lengths
n_traj: int = 0 # to store the number of trajectories in the dataset
def __init__(self, dataset, model_path, max_ep_len, max_len=24, scale=1000) -> None:
self.dataset = dataset
self.act_dim = len(dataset[0]["actions"][0])
self.state_dim = len(dataset[0]["observations"][0])
self.max_ep_len = max_ep_len
self.max_len = max_len
self.scale = scale
# calculate dataset stats for normalization of states
states = [] # List of all states of all sequences e.g. [s1,s2,s3,s1,s2,s3,s1,s2,s3]
traj_lens = [] # List of sequence length e.g. [3, 3, 3]
for obs in dataset["observations"]:
states.extend(obs)
traj_lens.append(len(obs))
self.n_traj = len(traj_lens)
states = np.vstack(states)
self.state_mean, self.state_std = np.mean(states, axis=0), np.std(states, axis=0) + 1e-6
if self.max_ep_len > 4096:
warnings.warn("max_ep_len over 4096. Error while training expected, please lower max_ep_len")
np.save(f'{model_path}/state_mean.npy', self.state_mean)
np.save(f'{model_path}//state_std.npy', self.state_std)
traj_lens = np.array(traj_lens)
self.p_sample = traj_lens / sum(traj_lens)
def _discount_cumsum(self, x, gamma):
discount_cumsum = np.zeros_like(x)
discount_cumsum[-1] = x[-1]
for t in reversed(range(x.shape[0] - 1)):
discount_cumsum[t] = x[t] + gamma * discount_cumsum[t + 1]
return discount_cumsum
def __call__(self, features):
batch_size = len(features)
# this is a bit of a hack to be able to sample of a non-uniform distribution
batch_inds = np.random.choice(
np.arange(self.n_traj),
size=batch_size,
replace=True,
p=self.p_sample, # reweights so we sample according to timesteps
)
# a batch of dataset features
s, a, r, d, rtg, timesteps, mask = [], [], [], [], [], [], [] # mask?
for ind in batch_inds:
# for feature in features:
feature = self.dataset[int(ind)]
si = random.randint(0, len(feature["rewards"]) - 1)
# get sequences from dataset
s.append(np.array(feature["observations"][si: si + self.max_len]).reshape(1, -1, self.state_dim))
a.append(np.array(feature["actions"][si: si + self.max_len]).reshape(1, -1, self.act_dim))
r.append(np.array(feature["rewards"][si: si + self.max_len]).reshape(1, -1, 1))
d.append(np.array(feature["dones"][si: si + self.max_len]).reshape(1, -1))
timesteps.append(np.arange(si, si + s[-1].shape[1]).reshape(1, -1))
timesteps[-1][timesteps[-1] >= self.max_ep_len] = self.max_ep_len - 1 # padding cutoff
rtg.append(
self._discount_cumsum(np.array(feature["rewards"][si:]), gamma=1.0)[
: s[-1].shape[1] # TL: check the +1 removed here
].reshape(1, -1, 1)
)
if rtg[-1].shape[1] < s[-1].shape[1]:
print("if true")
rtg[-1] = np.concatenate([rtg[-1], np.zeros((1, 1, 1))], axis=1)
# padding and state + reward normalization
tlen = s[-1].shape[1]
s[-1] = np.concatenate([np.zeros((1, self.max_len - tlen, self.state_dim)), s[-1]], axis=1)
s[-1] = (s[-1] - self.state_mean) / self.state_std
a[-1] = np.concatenate(
[np.ones((1, self.max_len - tlen, self.act_dim)) * -10.0, a[-1]],
axis=1,
)
r[-1] = np.concatenate([np.zeros((1, self.max_len - tlen, 1)), r[-1]], axis=1)
d[-1] = np.concatenate([np.ones((1, self.max_len - tlen)) * 2, d[-1]], axis=1)
rtg[-1] = np.concatenate([np.zeros((1, self.max_len - tlen, 1)), rtg[-1]], axis=1) / self.scale
timesteps[-1] = np.concatenate([np.zeros((1, self.max_len - tlen)), timesteps[-1]], axis=1)
mask.append(np.concatenate([np.zeros((1, self.max_len - tlen)), np.ones((1, tlen))], axis=1))
s = torch.from_numpy(np.concatenate(s, axis=0)).float()
a = torch.from_numpy(np.concatenate(a, axis=0)).float()
r = torch.from_numpy(np.concatenate(r, axis=0)).float()
d = torch.from_numpy(np.concatenate(d, axis=0))
rtg = torch.from_numpy(np.concatenate(rtg, axis=0)).float()
timesteps = torch.from_numpy(np.concatenate(timesteps, axis=0)).long()
mask = torch.from_numpy(np.concatenate(mask, axis=0)).float()
# print("rtg", rtg)
return {
"states": s,
"actions": a,
"rewards": r,
"returns_to_go": rtg,
"timesteps": timesteps,
"attention_mask": mask,
}
class TrainableDT(DecisionTransformerModel):
def __init__(self, config):
super().__init__(config)
def forward(self, **kwargs):
output = super().forward(**kwargs)
# return_priority = 0.1
# add the DT loss
action_preds = output[1]
action_targets = kwargs["actions"]
# return_preds = output[2]
# return_targets = kwargs["rewards"]
attention_mask = kwargs["attention_mask"]
act_dim = action_preds.shape[2]
# ret_dim = return_preds.shape[2]
action_preds = action_preds.reshape(-1, act_dim)[attention_mask.reshape(-1) > 0]
action_targets = action_targets.reshape(-1, act_dim)[attention_mask.reshape(-1) > 0]
# return_preds = return_preds.reshape(-1, ret_dim)[attention_mask.reshape(-1) > 0]
# return_targets = return_targets.reshape(-1, ret_dim)[attention_mask.reshape(-1) > 0]
loss = torch.mean((action_preds - action_targets) ** 2) # + return_priority * (return_preds - return_targets) ** 2)
return {"loss": loss} # TODO test other loss functions
def original_forward(self, **kwargs):
return super().forward(**kwargs)
def train():
model_name = "DT_e345_1"
offline_data_path = "data/DT_data/ensemble345_33.pkl"
max_ep_len = 719
max_len = 12
scale = 1
context_length = 12
lr = 1e-4
epochs = 10
batch_size = 64
weight_decay = 1e-4
warmup_ratio = 0 # 0.1
try:
dataset = load_from_disk(offline_data_path)
except FileNotFoundError:
raise FileNotFoundError(f'Dataset at path {offline_data_path} not found. You can create one using evaluation.py and set generate_data = True')
model_path = f"my_models/Decision_Transformer/{model_name}"
if not os.path.exists(model_path):
os.makedirs(model_path)
print(f'created path {model_path}')
collator = DecisionTransformerCityLearnDataCollator(dataset, model_path, max_ep_len, max_len, scale)
config = DecisionTransformerConfig(state_dim=collator.state_dim, act_dim=collator.act_dim, max_length=context_length)
model = TrainableDT(config)
training_args = TrainingArguments(
output_dir=model_name,
overwrite_output_dir=False,
remove_unused_columns=False,
num_train_epochs=epochs,
per_device_train_batch_size=batch_size,
learning_rate=lr,
weight_decay=weight_decay,
warmup_ratio=warmup_ratio,
optim="adamw_torch",
max_grad_norm=1,
logging_dir=f"logs/DT_logs/{model_name}",
logging_steps=1,
save_steps=1,
load_best_model_at_end=False,
push_to_hub=False,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
data_collator=collator,
)
trainer.train()
# print(trainer.state.log_history)
path_to_save = 'my_models/Decision_Transformer/' + model_name
model.save_pretrained(path_to_save)
print('Model saved to: ' + path_to_save)
if __name__ == '__main__':
train()