Movement
Movement (low level)¤
view
¤
view(*shape) -> Tensor
.view
is an alias for .reshape
.
Source code in tinygrad/tensor.py
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reshape
¤
reshape(shape, *args) -> Tensor
Returns a tensor with the same data as the original tensor but with a different shape.
shape
can be passed as a tuple or as separate arguments.
t = Tensor.arange(6)
print(t.reshape(2, 3).numpy())
[[0 1 2]
[3 4 5]]
Source code in tinygrad/tensor.py
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expand
¤
expand(shape, *args) -> Tensor
Returns a tensor that is expanded to the shape that is specified. Expand can also increase the number of dimensions that a tensor has.
Passing a -1
or None
to a dimension means that its size will not be changed.
t = Tensor([1, 2, 3])
print(t.expand(4, -1).numpy())
[[1 2 3]
[1 2 3]
[1 2 3]
[1 2 3]]
Source code in tinygrad/tensor.py
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permute
¤
permute(order, *args) -> Tensor
Returns a tensor that is a permutation of the original tensor.
The new tensor has the same data as the original tensor but with the dimensions permuted according to the order specified.
order
can be passed as a tuple or as separate arguments.
t = Tensor.arange(6).reshape(2, 3)
print(t.numpy())
[[0 1 2]
[3 4 5]]
print(t.permute(1, 0).numpy())
[[0 3]
[1 4]
[2 5]]
Source code in tinygrad/tensor.py
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flip
¤
flip(axis, *args) -> Tensor
Returns a tensor that reverses the order of the original tensor along given axis
.
axis
can be passed as a tuple or as separate arguments.
t = Tensor.arange(6).reshape(2, 3)
print(t.numpy())
[[0 1 2]
[3 4 5]]
print(t.flip(0).numpy())
[[3 4 5]
[0 1 2]]
print(t.flip((0, 1)).numpy())
[[5 4 3]
[2 1 0]]
Source code in tinygrad/tensor.py
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shrink
¤
Returns a tensor that shrinks the each axis based on input arg.
arg
must have the same length as self.ndim
.
For each axis, it can be None
, which means no shrink, or a tuple (start, end)
that works the same as Python slice.
t = Tensor.arange(9).reshape(3, 3)
print(t.numpy())
[[0 1 2]
[3 4 5]
[6 7 8]]
print(t.shrink(((None, (1, 3)))).numpy())
[[1 2]
[4 5]
[7 8]]
print(t.shrink((((0, 2), (0, 2)))).numpy())
[[0 1]
[3 4]]
Source code in tinygrad/tensor.py
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pad
¤
pad(
padding: Union[
Sequence[sint],
Sequence[Optional[Tuple[sint, sint]]],
],
mode: str = "constant",
value: float = 0.0,
) -> Tensor
Returns a tensor with padding applied based on the input padding
.
padding
supports two padding structures:
- Flat padding: (padding_left, padding_right, padding_top, padding_bottom, ...)
- This structure matches PyTorch's pad.
-
padding
length must be even. -
Group padding: (..., (padding_top, padding_bottom), (padding_left, padding_right))
- This structure matches pad for jax, numpy, tensorflow and others.
- For each axis, padding can be
None
, meaning no padding, or a tuple(start, end)
. padding
must have the same length asself.ndim
.
Padding values can be negative, resulting in dimension shrinks that work similarly to Python negative slices.
Padding modes is selected with mode
which supports constant
, reflect
and replicate
.
t = Tensor.arange(9).reshape(1, 1, 3, 3)
print(t.numpy())
[[[[0 1 2]
[3 4 5]
[6 7 8]]]]
print(t.pad((1, 2, 0, -1)).numpy())
[[[[0 0 1 2 0 0]
[0 3 4 5 0 0]]]]
print(t.pad(((None, None, (0, -1), (1, 2)))).numpy())
[[[[0 0 1 2 0 0]
[0 3 4 5 0 0]]]]
print(t.pad((1, 2, 0, -1), value=-float('inf')).numpy())
[[[[-inf 0. 1. 2. -inf -inf]
[-inf 3. 4. 5. -inf -inf]]]]
Source code in tinygrad/tensor.py
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Movement (high level)¤
gather
¤
Gathers values along an axis specified by dim
.
t = Tensor([[1, 2], [3, 4]])
print(t.numpy())
[[1 2]
[3 4]]
print(t.gather(1, Tensor([[0, 0], [1, 0]])).numpy())
[[1 1]
[4 3]]
Source code in tinygrad/tensor.py
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cat
¤
Concatenates self with other Tensor
in args
along an axis specified by dim
.
All tensors must have the same shape except in the concatenating dimension.
t0, t1, t2 = Tensor([[1, 2]]), Tensor([[3, 4]]), Tensor([[5, 6]])
print(t0.cat(t1, t2, dim=0).numpy())
[[1 2]
[3 4]
[5 6]]
print(t0.cat(t1, t2, dim=1).numpy())
[[1 2 3 4 5 6]]
Source code in tinygrad/tensor.py
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stack
¤
Concatenates self with other Tensor
in args
along a new dimension specified by dim
.
t0, t1, t2 = Tensor([1, 2]), Tensor([3, 4]), Tensor([5, 6])
print(t0.stack(t1, t2, dim=0).numpy())
[[1 2]
[3 4]
[5 6]]
print(t0.stack(t1, t2, dim=1).numpy())
[[1 3 5]
[2 4 6]]
Source code in tinygrad/tensor.py
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repeat
¤
repeat(repeats, *args) -> Tensor
Repeats tensor number of times along each dimension specified by repeats
.
repeats
can be passed as a tuple or as separate arguments.
t = Tensor([1, 2, 3])
print(t.repeat(4, 2).numpy())
[[1 2 3 1 2 3]
[1 2 3 1 2 3]
[1 2 3 1 2 3]
[1 2 3 1 2 3]]
print(t.repeat(4, 2, 1).shape)
(4, 2, 3)
Source code in tinygrad/tensor.py
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repeat_interleave
¤
Repeat elements of a tensor.
t = Tensor([1, 2, 3])
print(t.repeat_interleave(2).numpy())
[1 1 2 2 3 3]
Source code in tinygrad/tensor.py
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split
¤
Splits the tensor into chunks along the dimension specified by dim
.
If sizes
is an integer, it splits into equally sized chunks if possible, otherwise the last chunk will be smaller.
If sizes
is a list, it splits into len(sizes)
chunks with size in dim
according to size
.
t = Tensor.arange(10).reshape(5, 2)
print(t.numpy())
[[0 1]
[2 3]
[4 5]
[6 7]
[8 9]]
split = t.split(2)
print("\n".join([repr(x.numpy()) for x in split]))
array([[0, 1],
[2, 3]], dtype=int32)
array([[4, 5],
[6, 7]], dtype=int32)
array([[8, 9]], dtype=int32)
split = t.split([1, 4])
print("\n".join([repr(x.numpy()) for x in split]))
array([[0, 1]], dtype=int32)
array([[2, 3],
[4, 5],
[6, 7],
[8, 9]], dtype=int32)
Source code in tinygrad/tensor.py
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chunk
¤
Splits the tensor into chunks
number of chunks along the dimension dim
.
If the tensor size along dim
is not divisible by chunks
, all returned chunks will be the same size except the last one.
The function may return fewer than the specified number of chunks.
chunked = Tensor.arange(11).chunk(6)
print("\n".join([repr(x.numpy()) for x in chunked]))
array([0, 1], dtype=int32)
array([2, 3], dtype=int32)
array([4, 5], dtype=int32)
array([6, 7], dtype=int32)
array([8, 9], dtype=int32)
array([10], dtype=int32)
chunked = Tensor.arange(12).chunk(6)
print("\n".join([repr(x.numpy()) for x in chunked]))
array([0, 1], dtype=int32)
array([2, 3], dtype=int32)
array([4, 5], dtype=int32)
array([6, 7], dtype=int32)
array([8, 9], dtype=int32)
array([10, 11], dtype=int32)
chunked = Tensor.arange(13).chunk(6)
print("\n".join([repr(x.numpy()) for x in chunked]))
array([0, 1, 2], dtype=int32)
array([3, 4, 5], dtype=int32)
array([6, 7, 8], dtype=int32)
array([ 9, 10, 11], dtype=int32)
array([12], dtype=int32)
Source code in tinygrad/tensor.py
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meshgrid
¤
meshgrid(
*args: Tensor,
indexing: Union[Literal["ij"], Literal["xy"]] = "ij"
) -> Tuple[Tensor, ...]
Generates coordinate matrices from coordinate vectors. Input tensors can be scalars or 1D tensors.
indexing
determines how the output grids are aligned.
ij
indexing follows matrix-style indexing and xy
indexing follows Cartesian-style indexing.
x, y = Tensor([1, 2, 3]), Tensor([4, 5, 6])
grid_x, grid_y = x.meshgrid(y)
print(grid_x.numpy())
print(grid_y.numpy())
[[1 1 1]
[2 2 2]
[3 3 3]]
[[4 5 6]
[4 5 6]
[4 5 6]]
grid_x, grid_y = x.meshgrid(y, indexing="xy")
print(grid_x.numpy())
print(grid_y.numpy())
[[1 2 3]
[1 2 3]
[1 2 3]]
[[4 4 4]
[5 5 5]
[6 6 6]]
Source code in tinygrad/tensor.py
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squeeze
¤
Returns a tensor with specified dimensions of input of size 1 removed.
If dim
is not specified, all dimensions with size 1 are removed.
t = Tensor.zeros(2, 1, 2, 1, 2)
print(t.squeeze().shape)
(2, 2, 2)
print(t.squeeze(0).shape)
(2, 1, 2, 1, 2)
print(t.squeeze(1).shape)
(2, 2, 1, 2)
Source code in tinygrad/tensor.py
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unsqueeze
¤
Returns a tensor with a new dimension of size 1 inserted at the specified dim
.
t = Tensor([1, 2, 3, 4])
print(t.unsqueeze(0).numpy())
[[1 2 3 4]]
print(t.unsqueeze(1).numpy())
[[1]
[2]
[3]
[4]]
Source code in tinygrad/tensor.py
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transpose
¤
transpose(dim0=1, dim1=0) -> Tensor
Returns a tensor that is a transposed version of the original tensor.
The given dimensions dim0
and dim1
are swapped.
t = Tensor.arange(6).reshape(2, 3)
print(t.numpy())
[[0 1 2]
[3 4 5]]
print(t.transpose(0, 1).numpy())
[[0 3]
[1 4]
[2 5]]
Source code in tinygrad/tensor.py
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flatten
¤
flatten(start_dim=0, end_dim=-1)
Flattens the tensor by reshaping it into a one-dimensional tensor.
If start_dim
or end_dim
are passed, only dimensions starting with start_dim
and ending with end_dim
are flattened.
t = Tensor.arange(8).reshape(2, 2, 2)
print(t.flatten().numpy())
[0 1 2 3 4 5 6 7]
print(t.flatten(start_dim=1).numpy())
[[0 1 2 3]
[4 5 6 7]]
Source code in tinygrad/tensor.py
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unflatten
¤
Unflattens dimension dim
of the tensor into multiple dimensions specified by sizes
. Tensor.flatten()
is the inverse of this function.
print(Tensor.ones(3, 4, 1).unflatten(1, (2, 2)).shape)
(3, 2, 2, 1)
print(Tensor.ones(3, 4, 1).unflatten(1, (-1, 2)).shape)
(3, 2, 2, 1)
print(Tensor.ones(5, 12, 3).unflatten(-2, (2, 2, 3, 1, 1)).shape)
(5, 2, 2, 3, 1, 1, 3)
Source code in tinygrad/tensor.py
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