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)
/tmp/tmp4q2ykbbk/421d9363b607db852c2d5a9757463d2a8380be6c/treevalue/tree/integration/torch.py:18: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.
register_for_torch(TreeValue)
/tmp/tmp4q2ykbbk/421d9363b607db852c2d5a9757463d2a8380be6c/treevalue/tree/integration/torch.py:19: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.
register_for_torch(FastTreeValue)
[3]:
t
[3]:
[4]:
t.a
[4]:
tensor([[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]])
[5]:
%timeit t.a
45.6 ns ± 0.605 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
48.9 ns ± 0.3 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([[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]]),
x: Batch(
c: tensor([[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]]),
),
)
[10]:
b.a
[10]:
tensor([[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]])
[11]:
%timeit b.a
40.8 ns ± 0.288 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([[ 0.8850, -0.5676, -0.1094],
[ 1.8170, 0.2699, -0.6334]]),
x: Batch(
c: tensor([[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]]),
),
)
[13]:
%timeit b.a = new_value
372 ns ± 2.69 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
Initialization¶
TreeValue’s Initialization¶
[14]:
%timeit FastTreeValue(_TREE_DATA_1)
636 ns ± 5.22 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
Tianshou Batch’s Initialization¶
[15]:
%timeit Batch(**_TREE_DATA_1)
8.49 µs ± 36.7 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)
126 µs ± 332 ns 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)
124 µs ± 629 ns 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)
23.7 µs ± 55.7 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)
22.2 µs ± 281 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)
49.5 µs ± 234 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(
x: Batch(
c: tensor([[[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]],
[[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]],
[[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]],
[[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]],
[[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]],
[[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]],
[[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]],
[[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]]]),
),
a: tensor([[[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]],
[[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]],
[[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]],
[[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]],
[[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]],
[[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]],
[[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]],
[[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]]]),
)
[26]:
%timeit Batch.stack(batches)
62.9 µs ± 560 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
[27]:
Batch.cat(batches)
[27]:
Batch(
x: Batch(
c: tensor([[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196],
[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196],
[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196],
[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196],
[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196],
[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196],
[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196],
[-0.8865, 0.6126, -0.4188, 1.1865],
[-0.4631, 1.3203, 0.9765, -0.9233],
[ 1.1025, -1.2060, -1.0994, 0.3196]]),
),
a: tensor([[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786],
[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786],
[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786],
[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786],
[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786],
[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786],
[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786],
[ 2.9557, 0.4651, 1.6729],
[-1.9608, 0.4644, 0.5786]]),
)
[28]:
%timeit Batch.cat(batches)
117 µs ± 803 ns 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))
279 µs ± 4.65 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
[ ]: