Creation
Creation (basic)¤
empty
staticmethod
¤
empty(*shape, **kwargs)
Creates an empty tensor with the given shape.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
t = Tensor.empty(2, 3)
print(t.shape)
(2, 3)
Source code in tinygrad/tensor.py
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zeros
staticmethod
¤
zeros(*shape, **kwargs) -> Tensor
Creates a tensor with the given shape, filled with zeros.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
print(Tensor.zeros(2, 3).numpy())
[[0. 0. 0.]
[0. 0. 0.]]
print(Tensor.zeros(2, 3, dtype=dtypes.int32).numpy())
[[0 0 0]
[0 0 0]]
Source code in tinygrad/tensor.py
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ones
staticmethod
¤
ones(*shape, **kwargs) -> Tensor
Creates a tensor with the given shape, filled with ones.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
print(Tensor.ones(2, 3).numpy())
[[1. 1. 1.]
[1. 1. 1.]]
print(Tensor.ones(2, 3, dtype=dtypes.int32).numpy())
[[1 1 1]
[1 1 1]]
Source code in tinygrad/tensor.py
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full
staticmethod
¤
Creates a tensor with the given shape, filled with the given value.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
print(Tensor.full((2, 3), 42).numpy())
[[42 42 42]
[42 42 42]]
print(Tensor.full((2, 3), False).numpy())
[[False False False]
[False False False]]
Source code in tinygrad/tensor.py
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arange
staticmethod
¤
arange(start, stop=None, step=1, **kwargs) -> Tensor
Returns a 1-D tensor of size ceil((stop - start) / step)
with values from [start, stop)
, with spacing between values given by step
.
If stop
is not specified, values are generated from [0, start)
with the given step
.
If stop
is specified, values are generated from [start, stop)
with the given step
.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
print(Tensor.arange(5).numpy())
[0 1 2 3 4]
print(Tensor.arange(5, 10).numpy())
[5 6 7 8 9]
print(Tensor.arange(5, 10, 2).numpy())
[5 7 9]
print(Tensor.arange(5.5, 10, 2).numpy())
[5.5 7.5 9.5]
Source code in tinygrad/tensor.py
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eye
staticmethod
¤
Returns a 2-D tensor with n
rows and m
columns, with ones on the diagonal and zeros elsewhere.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
print(Tensor.eye(3).numpy())
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
print(Tensor.eye(2, 4).numpy())
[[1. 0. 0. 0.]
[0. 1. 0. 0.]]
Source code in tinygrad/tensor.py
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full_like
¤
Creates a tensor with the same shape as self
, filled with the given value.
If dtype
is not specified, the dtype of self
is used.
You can pass in the device
keyword argument to control device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
t = Tensor.ones(2, 3)
print(Tensor.full_like(t, 42).numpy())
[[42. 42. 42.]
[42. 42. 42.]]
Source code in tinygrad/tensor.py
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zeros_like
¤
zeros_like(**kwargs) -> Tensor
Creates a tensor with the same shape as self
, filled with zeros.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
t = Tensor.ones(2, 3)
print(Tensor.zeros_like(t).numpy())
[[0. 0. 0.]
[0. 0. 0.]]
Source code in tinygrad/tensor.py
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ones_like
¤
ones_like(**kwargs) -> Tensor
Creates a tensor with the same shape as self
, filled with ones.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
t = Tensor.zeros(2, 3)
print(Tensor.ones_like(t).numpy())
[[1. 1. 1.]
[1. 1. 1.]]
Source code in tinygrad/tensor.py
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from_blob
staticmethod
¤
Exposes the pointer as a Tensor without taking ownership of the original data. The pointer must remain valid for the entire lifetime of the created Tensor.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
Source code in tinygrad/tensor.py
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Creation (random)¤
manual_seed
staticmethod
¤
manual_seed(seed=0)
Sets the seed for random operations.
Tensor.manual_seed(42)
print(Tensor.rand(5).numpy())
print(Tensor.rand(5).numpy())
[0.997 0.5899 0.2225 0.7551 0.9057]
[0.6162 0.6213 0.9791 0.7851 0.4178]
Tensor.manual_seed(42) # reset to the same seed
print(Tensor.rand(5).numpy())
print(Tensor.rand(5).numpy())
[0.997 0.5899 0.2225 0.7551 0.9057]
[0.6162 0.6213 0.9791 0.7851 0.4178]
Source code in tinygrad/tensor.py
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rand
staticmethod
¤
Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval [0, 1)
.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
Tensor.manual_seed(42)
t = Tensor.rand(2, 3)
print(t.numpy())
[[0.997 0.5899 0.2225]
[0.7551 0.9057 0.8649]]
Source code in tinygrad/tensor.py
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randn
staticmethod
¤
Creates a tensor with the given shape, filled with random values from a normal distribution with mean 0
and standard deviation 1
.
If dtype
is not specified, the default type is used.
You can pass in the device
keyword argument to control device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
Tensor.manual_seed(42)
print(Tensor.randn(2, 3).numpy())
[[ 0.9779 0.4678 0.5526]
[-0.3288 -0.8555 0.2753]]
Source code in tinygrad/tensor.py
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randint
staticmethod
¤
randint(*shape, low=0, high=10, **kwargs) -> Tensor
Creates a tensor with the given shape, filled with random integer values generated uniformly from the interval [low, high)
.
If dtype
is not specified, the default type is used.
You can pass in the device
keyword argument to control device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
Tensor.manual_seed(42)
print(Tensor.randint(2, 3, low=5, high=10).numpy())
[[9 7 6]
[8 9 9]]
Source code in tinygrad/tensor.py
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normal
staticmethod
¤
normal(*shape, mean=0.0, std=1.0, **kwargs) -> Tensor
Creates a tensor with the given shape, filled with random values from a normal distribution with the given mean
and standard deviation std
.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
Tensor.manual_seed(42)
print(Tensor.normal(2, 3, mean=10, std=2).numpy())
[[11.9557 10.9356 11.1053]
[ 9.3423 8.289 10.5505]]
Source code in tinygrad/tensor.py
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uniform
staticmethod
¤
uniform(*shape, low=0.0, high=1.0, **kwargs) -> Tensor
Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval [low, high)
.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
Tensor.manual_seed(42)
print(Tensor.uniform(2, 3, low=2, high=10).numpy())
[[9.9763 6.7193 3.7804]
[8.0404 9.2452 8.9191]]
Source code in tinygrad/tensor.py
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scaled_uniform
staticmethod
¤
scaled_uniform(*shape, **kwargs) -> Tensor
Creates a tensor with the given shape, filled with random values from a uniform distribution
over the interval [-prod(shape)**-0.5, prod(shape)**-0.5)
.
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
Tensor.manual_seed(42)
print(Tensor.scaled_uniform(2, 3).numpy())
[[ 0.4058 0.0734 -0.2265]
[ 0.2082 0.3312 0.2979]]
Source code in tinygrad/tensor.py
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glorot_uniform
staticmethod
¤
glorot_uniform(*shape, **kwargs) -> Tensor
https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
Tensor.manual_seed(42)
print(Tensor.glorot_uniform(2, 3).numpy())
[[ 1.0889 0.197 -0.6079]
[ 0.5588 0.8887 0.7994]]
Source code in tinygrad/tensor.py
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kaiming_uniform
staticmethod
¤
https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_uniform_
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
Tensor.manual_seed(42)
print(Tensor.kaiming_uniform(2, 3).numpy())
[[ 1.4058 0.2543 -0.7847]
[ 0.7214 1.1473 1.032 ]]
Source code in tinygrad/tensor.py
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kaiming_normal
staticmethod
¤
https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_normal_
You can pass in dtype
and device
keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.
Tensor.manual_seed(42)
print(Tensor.kaiming_normal(2, 3).numpy())
[[ 0.7984 0.3819 0.4512]
[-0.2685 -0.6985 0.2247]]
Source code in tinygrad/tensor.py
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