Comparison Between TreeValue and Tianshou Batch¶
In this section, we will take a look at the feature and performance of the Tianshou Batch library, which is developed by Tsinghua Machine Learning Group.
Before starting the comparison, let us define some thing.
[1]:
import torch
_TREE_DATA_1 = {'a': 1, 'b': 2, 'x': {'c': 3, 'd': 4}}
_TREE_DATA_2 = {
'a': torch.randn(2, 3),
'x': {
'c': torch.randn(3, 4)
},
}
_TREE_DATA_3 = {
'obs': torch.randn(4, 84, 84),
'action': torch.randint(0, 6, size=(1,)),
'reward': torch.rand(1),
}
Read and Write Operation¶
Reading and writing are the two most common operations in the tree data structure based on the data model (TreeValue and Tianshou Batch both belong to this type), so this section will compare the reading and writing performance of these two libraries.
TreeValue’s Get and Set¶
[2]:
from treevalue import FastTreeValue
t = FastTreeValue(_TREE_DATA_2)
[3]:
t
[3]:
[4]:
t.a
[4]:
tensor([[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]])
[5]:
%timeit t.a
85 ns ± 1.33 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
[6]:
new_value = torch.randn(2, 3)
t.a = new_value
t
[6]:
[7]:
%timeit t.a = new_value
95.8 ns ± 0.773 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
Tianshou Batch’s Get and Set¶
[8]:
from tianshou.data import Batch
b = Batch(**_TREE_DATA_2)
[9]:
b
[9]:
Batch(
a: tensor([[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]]),
x: Batch(
c: tensor([[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]]),
),
)
[10]:
b.a
[10]:
tensor([[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]])
[11]:
%timeit b.a
78.8 ns ± 0.529 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
[12]:
new_value = torch.randn(2, 3)
b.a = new_value
b
[12]:
Batch(
a: tensor([[ 0.1180, 0.8688, 0.6846],
[ 1.0660, -0.9939, -0.9313]]),
x: Batch(
c: tensor([[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]]),
),
)
[13]:
%timeit b.a = new_value
660 ns ± 10.5 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
Initialization¶
TreeValue’s Initialization¶
[14]:
%timeit FastTreeValue(_TREE_DATA_1)
1.11 µs ± 14.8 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
Tianshou Batch’s Initialization¶
[15]:
%timeit Batch(**_TREE_DATA_1)
12.3 µs ± 117 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
Deep Copy Operation¶
[16]:
import copy
Deep Copy of TreeValue¶
[17]:
t3 = FastTreeValue(_TREE_DATA_3)
%timeit copy.deepcopy(t3)
176 µs ± 3.37 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
Deep Copy of Tianshou Batch¶
[18]:
b3 = Batch(**_TREE_DATA_3)
%timeit copy.deepcopy(b3)
178 µs ± 3.42 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
Stack, Concat and Split Operation¶
Performance of TreeValue¶
[19]:
trees = [FastTreeValue(_TREE_DATA_2) for _ in range(8)]
[20]:
t_stack = FastTreeValue.func(subside=True)(torch.stack)
t_stack(trees)
[20]:
[21]:
%timeit t_stack(trees)
38.7 µs ± 567 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
[22]:
t_cat = FastTreeValue.func(subside=True)(torch.cat)
t_cat(trees)
[22]:
[23]:
%timeit t_cat(trees)
36.8 µs ± 353 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
[24]:
t_split = FastTreeValue.func(rise=True)(torch.split)
tree = FastTreeValue({
'obs': torch.randn(8, 4, 84, 84),
'action': torch.randint(0, 6, size=(8, 1,)),
'reward': torch.rand(8, 1),
})
%timeit t_split(tree, 1)
74.1 µs ± 523 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
Performance of Tianshou Batch¶
[25]:
batches = [Batch(**_TREE_DATA_2) for _ in range(8)]
Batch.stack(batches)
[25]:
Batch(
a: tensor([[[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]],
[[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]],
[[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]],
[[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]],
[[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]],
[[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]],
[[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]],
[[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]]]),
x: Batch(
c: tensor([[[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]],
[[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]],
[[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]],
[[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]],
[[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]],
[[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]],
[[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]],
[[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]]]),
),
)
[26]:
%timeit Batch.stack(batches)
97.3 µs ± 1.02 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
[27]:
Batch.cat(batches)
[27]:
Batch(
a: tensor([[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826],
[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826],
[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826],
[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826],
[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826],
[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826],
[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826],
[-0.4161, 0.2688, -1.0056],
[-0.9648, -1.3866, 0.7826]]),
x: Batch(
c: tensor([[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726],
[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726],
[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726],
[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726],
[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726],
[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726],
[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726],
[ 0.1343, -1.2773, -0.8435, 0.2540],
[ 1.9354, -0.1676, -1.5351, -0.1408],
[ 0.4617, 0.2551, 0.8022, 0.3726]]),
),
)
[28]:
%timeit Batch.cat(batches)
179 µs ± 2.39 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
[29]:
batch = Batch({
'obs': torch.randn(8, 4, 84, 84),
'action': torch.randint(0, 6, size=(8, 1,)),
'reward': torch.rand(8, 1)}
)
%timeit list(Batch.split(batch, 1, shuffle=False, merge_last=True))
437 µs ± 5.8 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
[ ]: