import torch
from treevalue import TreeValue
from treevalue.tree.common import TreeStorage
from .base import doc_from_base, func_treelize
from ..stream import stream_call
from ...utils import args_mapping
__all__ = [
'tensor', 'as_tensor', 'clone',
'zeros', 'zeros_like',
'randn', 'randn_like',
'rand', 'rand_like',
'randint', 'randint_like',
'ones', 'ones_like',
'full', 'full_like',
'empty', 'empty_like',
]
args_treelize = args_mapping(lambda i, x: TreeValue(x) if isinstance(x, (dict, TreeStorage, TreeValue)) else x)
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def tensor(data, *args, **kwargs):
"""
In ``treetensor``, you can create a tree tensor with simple data structure.
Examples::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.tensor(True) # the same as torch.tensor(True)
tensor(True)
>>> ttorch.tensor([1, 2, 3]) # the same as torch.tensor([1, 2, 3])
tensor([1, 2, 3])
>>> ttorch.tensor({'a': 1, 'b': [1, 2, 3], 'c': [[True, False], [False, True]]})
<Tensor 0x7ff363bbcc50>
├── a --> tensor(1)
├── b --> tensor([1, 2, 3])
└── c --> tensor([[ True, False],
[False, True]])
"""
return stream_call(torch.tensor, data, *args, **kwargs)
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def as_tensor(data, *args, **kwargs):
"""
Convert the data into a :class:`treetensor.torch.Tensor` or :class:`torch.Tensor`.
Examples::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.as_tensor(True)
tensor(True)
>>> ttorch.as_tensor([1, 2, 3], dtype=torch.float32)
tensor([1., 2., 3.])
>>> ttorch.as_tensor({
... 'a': torch.tensor([1, 2, 3]),
... 'b': {'x': [[4, 5], [6, 7]]}
... }, dtype=torch.float32)
<Tensor 0x7fc2b80c25c0>
├── a --> tensor([1., 2., 3.])
└── b --> <Tensor 0x7fc2b80c24e0>
└── x --> tensor([[4., 5.],
[6., 7.]])
"""
return stream_call(torch.as_tensor, data, *args, **kwargs)
# noinspection PyShadowingBuiltins
[docs]@doc_from_base()
@func_treelize()
def clone(input, *args, **kwargs):
"""
In ``treetensor``, you can create a clone of the original tree with :func:`treetensor.torch.clone`.
Examples::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.clone(torch.tensor([[1, 2], [3, 4]]))
tensor([[1, 2],
[3, 4]])
>>> ttorch.clone(ttorch.tensor({
... 'a': [[1, 2], [3, 4]],
... 'b': {'x': [[5], [6], [7]]},
... }))
<Tensor 0x7f2a820ba5e0>
├── a --> tensor([[1, 2],
│ [3, 4]])
└── b --> <Tensor 0x7f2a820aaf70>
└── x --> tensor([[5],
[6],
[7]])
"""
return stream_call(torch.clone, input, *args, **kwargs)
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def zeros(*args, **kwargs):
"""
In ``treetensor``, you can use ``zeros`` to create a tree of tensors with all zeros.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.zeros(2, 3) # the same as torch.zeros(2, 3)
tensor([[0., 0., 0.],
[0., 0., 0.]])
>>> ttorch.zeros({'a': (2, 3), 'b': {'x': (4, )}})
<Tensor 0x7f5f6ccf1ef0>
├── a --> tensor([[0., 0., 0.],
│ [0., 0., 0.]])
└── b --> <Tensor 0x7f5fe0107208>
└── x --> tensor([0., 0., 0., 0.])
"""
return stream_call(torch.zeros, *args, **kwargs)
# noinspection PyShadowingBuiltins
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def zeros_like(input, *args, **kwargs):
"""
In ``treetensor``, you can use ``zeros_like`` to create a tree of tensors with all zeros like another tree.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.zeros_like(torch.randn(2, 3)) # the same as torch.zeros_like(torch.randn(2, 3))
tensor([[0., 0., 0.],
[0., 0., 0.]])
>>> ttorch.zeros_like({
... 'a': torch.randn(2, 3),
... 'b': {'x': torch.randn(4, )},
... })
<Tensor 0x7ff363bb6128>
├── a --> tensor([[0., 0., 0.],
│ [0., 0., 0.]])
└── b --> <Tensor 0x7ff363bb6080>
└── x --> tensor([0., 0., 0., 0.])
"""
return stream_call(torch.zeros_like, input, *args, **kwargs)
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def randn(*args, **kwargs):
"""
In ``treetensor``, you can use ``randn`` to create a tree of tensors with numbers
obey standard normal distribution.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.randn(2, 3) # the same as torch.randn(2, 3)
tensor([[-0.8534, -0.5754, -0.2507],
[ 0.0826, -1.4110, 0.9748]])
>>> ttorch.randn({'a': (2, 3), 'b': {'x': (4, )}})
<Tensor 0x7ff363bb6518>
├── a --> tensor([[ 0.5398, 0.7529, -2.0339],
│ [-0.5722, -1.1900, 0.7945]])
└── b --> <Tensor 0x7ff363bb6438>
└── x --> tensor([-0.7181, 0.1670, -1.3587, -1.5129])
"""
return stream_call(torch.randn, *args, **kwargs)
# noinspection PyShadowingBuiltins
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def randn_like(input, *args, **kwargs):
"""
In ``treetensor``, you can use ``randn_like`` to create a tree of tensors with numbers
obey standard normal distribution like another tree.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.randn_like(torch.ones(2, 3)) # the same as torch.randn_like(torch.ones(2, 3))
tensor([[ 1.8436, 0.2601, 0.9687],
[ 1.6430, -0.1765, -1.1732]])
>>> ttorch.randn_like({
... 'a': torch.ones(2, 3),
... 'b': {'x': torch.ones(4, )},
... })
<Tensor 0x7ff3d6f3cb38>
├── a --> tensor([[-0.1532, 1.3965, -1.2956],
│ [-0.0750, 0.6475, 1.1421]])
└── b --> <Tensor 0x7ff3d6f420b8>
└── x --> tensor([ 0.1730, 1.6085, 0.6487, -1.1022])
"""
return stream_call(torch.randn_like, input, *args, **kwargs)
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def rand(*args, **kwargs):
"""
In ``treetensor``, you can use ``rand`` to create a tree of tensors with numbers
obey standard normal distribution.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.rand(2, 3) # the same as torch.rand(2, 3)
tensor([[-0.8534, -0.5754, -0.2507],
[ 0.0826, -1.4110, 0.9748]])
>>> ttorch.rand({'a': (2, 3), 'b': {'x': (4, )}})
<Tensor 0x7ff363bb6518>
├── a --> tensor([[ 0.5398, 0.7529, -2.0339],
│ [-0.5722, -1.1900, 0.7945]])
└── b --> <Tensor 0x7ff363bb6438>
└── x --> tensor([-0.7181, 0.1670, -1.3587, -1.5129])
"""
return stream_call(torch.rand, *args, **kwargs)
# noinspection PyShadowingBuiltins
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def rand_like(input, *args, **kwargs):
"""
In ``treetensor``, you can use ``rand_like`` to create a tree of tensors with numbers
obey standard normal distribution like another tree.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.rand_like(torch.ones(2, 3)) # the same as torch.rand_like(torch.ones(2, 3))
tensor([[ 1.8436, 0.2601, 0.9687],
[ 1.6430, -0.1765, -1.1732]])
>>> ttorch.rand_like({
... 'a': torch.ones(2, 3),
... 'b': {'x': torch.ones(4, )},
... })
<Tensor 0x7ff3d6f3cb38>
├── a --> tensor([[-0.1532, 1.3965, -1.2956],
│ [-0.0750, 0.6475, 1.1421]])
└── b --> <Tensor 0x7ff3d6f420b8>
└── x --> tensor([ 0.1730, 1.6085, 0.6487, -1.1022])
"""
return stream_call(torch.rand_like, input, *args, **kwargs)
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def randint(*args, **kwargs):
"""
In ``treetensor``, you can use ``randint`` to create a tree of tensors with numbers
in an integer range.
Examples::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.randint(10, (2, 3)) # the same as torch.randint(10, (2, 3))
tensor([[3, 4, 5],
[4, 5, 5]])
>>> ttorch.randint(10, {'a': (2, 3), 'b': {'x': (4, )}})
<Tensor 0x7ff363bb6438>
├── a --> tensor([[5, 3, 7],
│ [8, 1, 8]])
└── b --> <Tensor 0x7ff363bb6240>
└── x --> tensor([8, 8, 2, 4])
"""
return stream_call(torch.randint, *args, **kwargs)
# noinspection PyShadowingBuiltins
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def randint_like(input, *args, **kwargs):
"""
In ``treetensor``, you can use ``randint_like`` to create a tree of tensors with numbers
in an integer range.
Examples::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.randint_like(torch.ones(2, 3), 10) # the same as torch.randint_like(torch.ones(2, 3), 10)
tensor([[0., 5., 0.],
[2., 0., 9.]])
>>> ttorch.randint_like({
... 'a': torch.ones(2, 3),
... 'b': {'x': torch.ones(4, )},
... }, 10)
<Tensor 0x7ff363bb6748>
├── a --> tensor([[3., 6., 1.],
│ [8., 9., 5.]])
└── b --> <Tensor 0x7ff363bb6898>
└── x --> tensor([4., 4., 7., 1.])
"""
return stream_call(torch.randint_like, input, *args, **kwargs)
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def ones(*args, **kwargs):
"""
In ``treetensor``, you can use ``ones`` to create a tree of tensors with all ones.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.ones(2, 3) # the same as torch.ones(2, 3)
tensor([[1., 1., 1.],
[1., 1., 1.]])
>>> ttorch.ones({'a': (2, 3), 'b': {'x': (4, )}})
<Tensor 0x7ff363bb6eb8>
├── a --> tensor([[1., 1., 1.],
│ [1., 1., 1.]])
└── b --> <Tensor 0x7ff363bb6dd8>
└── x --> tensor([1., 1., 1., 1.])
"""
return stream_call(torch.ones, *args, **kwargs)
# noinspection PyShadowingBuiltins
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def ones_like(input, *args, **kwargs):
"""
In ``treetensor``, you can use ``ones_like`` to create a tree of tensors with all ones like another tree.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.ones_like(torch.randn(2, 3)) # the same as torch.ones_like(torch.randn(2, 3))
tensor([[1., 1., 1.],
[1., 1., 1.]])
>>> ttorch.ones_like({
... 'a': torch.randn(2, 3),
... 'b': {'x': torch.randn(4, )},
... })
<Tensor 0x7ff363bbc320>
├── a --> tensor([[1., 1., 1.],
│ [1., 1., 1.]])
└── b --> <Tensor 0x7ff363bbc240>
└── x --> tensor([1., 1., 1., 1.])
"""
return stream_call(torch.ones_like, input, *args, **kwargs)
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def full(*args, **kwargs):
"""
In ``treetensor``, you can use ``ones`` to create a tree of tensors with the same value.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.full((2, 3), 2.3) # the same as torch.full((2, 3), 2.3)
tensor([[2.3000, 2.3000, 2.3000],
[2.3000, 2.3000, 2.3000]])
>>> ttorch.full({'a': (2, 3), 'b': {'x': (4, )}}, 2.3)
<Tensor 0x7ff363bbc7f0>
├── a --> tensor([[2.3000, 2.3000, 2.3000],
│ [2.3000, 2.3000, 2.3000]])
└── b --> <Tensor 0x7ff363bbc8d0>
└── x --> tensor([2.3000, 2.3000, 2.3000, 2.3000])
"""
return stream_call(torch.full, *args, **kwargs)
# noinspection PyShadowingBuiltins
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def full_like(input, *args, **kwargs):
"""
In ``treetensor``, you can use ``ones_like`` to create a tree of tensors with
all the same value of like another tree.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.full_like(torch.randn(2, 3), 2.3) # the same as torch.full_like(torch.randn(2, 3), 2.3)
tensor([[2.3000, 2.3000, 2.3000],
[2.3000, 2.3000, 2.3000]])
>>> ttorch.full_like({
... 'a': torch.randn(2, 3),
... 'b': {'x': torch.randn(4, )},
... }, 2.3)
<Tensor 0x7ff363bb6cf8>
├── a --> tensor([[2.3000, 2.3000, 2.3000],
│ [2.3000, 2.3000, 2.3000]])
└── b --> <Tensor 0x7ff363bb69e8>
└── x --> tensor([2.3000, 2.3000, 2.3000, 2.3000])
"""
return stream_call(torch.full_like, input, *args, **kwargs)
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def empty(*args, **kwargs):
"""
In ``treetensor``, you can use ``ones`` to create a tree of tensors with
the uninitialized values.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.empty(2, 3) # the same as torch.empty(2, 3)
tensor([[-1.3267e-36, 3.0802e-41, 2.3000e+00],
[ 2.3000e+00, 2.3000e+00, 2.3000e+00]])
>>> ttorch.empty({'a': (2, 3), 'b': {'x': (4, )}})
<Tensor 0x7ff363bb6080>
├── a --> tensor([[-3.6515e+14, 4.5900e-41, -1.3253e-36],
│ [ 3.0802e-41, 2.3000e+00, 2.3000e+00]])
└── b --> <Tensor 0x7ff363bb66d8>
└── x --> tensor([-3.6515e+14, 4.5900e-41, -3.8091e-38, 3.0802e-41])
"""
return stream_call(torch.empty, *args, **kwargs)
# noinspection PyShadowingBuiltins
[docs]@doc_from_base()
@args_treelize
@func_treelize()
def empty_like(input, *args, **kwargs):
"""
In ``treetensor``, you can use ``ones_like`` to create a tree of tensors with
all the uninitialized values of like another tree.
Example::
>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.empty_like(torch.randn(2, 3)) # the same as torch.empty_like(torch.randn(2, 3), 2.3)
tensor([[-3.6515e+14, 4.5900e-41, -1.3266e-36],
[ 3.0802e-41, 4.4842e-44, 0.0000e+00]])
>>> ttorch.empty_like({
... 'a': torch.randn(2, 3),
... 'b': {'x': torch.randn(4, )},
... })
<Tensor 0x7ff363bbc780>
├── a --> tensor([[-3.6515e+14, 4.5900e-41, -3.6515e+14],
│ [ 4.5900e-41, 1.1592e-41, 0.0000e+00]])
└── b --> <Tensor 0x7ff3d6f3cb38>
└── x --> tensor([-1.3267e-36, 3.0802e-41, -3.8049e-38, 3.0802e-41])
"""
return stream_call(torch.empty_like, input, *args, **kwargs)