hidet.Tensor

class hidet.Tensor(shape, dtype, device, storage, layout=None, trace=None)

An n-dimension array, could be symbolic or concrete.

This class defines an n-dimension array.

Parameters

  • shape (Sequence[int]) – The shape of the tensor.

  • dtype (DataType or str) – The data type of the tensor.

  • device (Device or str) – The device of the tensor.

  • storage (Storage, optional) – The storage of the tensor. None indicates it is a symbolic tensor.

  • layout (DataLayout, optional) – The data layout of the tensor.

  • trace (Tuple[Operator, int], optional) – Where this tensor is derived from. A trace = (op, i) indicates that this tensor is the i-th output of the op operator.

shape

The shape of the tensor.

The shape is a tuple of integers indicating the size of the tensor along each dimension.

Returns

shape – The shape of the tensor.

Return type

Tuple[int, …]

dtype

The data type of the tensor.

Returns

dtype – The data type of the tensor.

Return type

DataType

device

The device of the tensor.

Returns

device – The device of the tensor.

Return type

Device

size

The number of elements in the tensor.

Returns

size – The number of elements in the tensor.

Return type

int

nbytes

The number of bytes of the tensor.

Returns

ret – The number of bytes.

Return type

int

storage

The storage of the tensor.

Returns

storage – The storage of the tensor.

Return type

Storage

trace

The producer and the index of outputs of the producer of this tensor.

This attribute is used to track how this tensor is computed. None indicates this is a leaf tensor where the value will be given by the user. Otherwise, it will be a tuple with (operator, index) where operator is the producer of this tensor and index is the index of the output of the operator.

Returns

trace – The trace of this tensor.

Return type

Tuple[Operator, int]

op

The operator that produces this tensor.

Returns

ret – The operator that produces this tensor. None indicates it is not traced.

Return type

hidet.graph.operator.Operator, optional

layout

The data layout of the tensor.

Note

This attribute is experimental and might change in the future.

Returns

layout – The data layout of the tensor.

Return type

DataLayout

tolist()[source]

Convert the tensor to a nested list of numbers.

Returns

ret – The nested list of numbers. The number of nested levels is equal to the rank of the tensor.

Return type

the nested list of numbers

to_device(device, /, *, stream=None)[source]

Move the tensor to the specified device.

Parameters

  • device (Device or str) – The device to move the tensor to.

  • stream (Stream or None) – The stream to use for the copy. If None, the current stream is used.

Returns

ret – The tensor on the specified device.

Return type

Tensor

astype(dtype)[source]

Cast the data type of current tensor.

Parameters

dtype (DataType or str) – The target data type to convert to.

Returns

ret – The tensor with the new data type.

Return type

Tensor

cpu()[source]

Create a copy of self tensor on cpu device.

If the current tensor is already on cpu device, self is returned.

Returns

ret – The new tensor or self.

Return type

Tensor

cuda(device=None)[source]

Create a copy of self tensor on cuda device.

If the current tensor is already on cuda device, self is returned.

Parameters

device (Device, optional) – The target cuda device. None indicates the current cuda device.

Returns

ret – The new tensor or self.

Return type

Tensor

copy()[source]

Create a copy of current tensor.

Returns

ret – A new tensor with the same contents as the current one.

Return type

Tensor

cpu_async(stream=None)[source]

Copy the tensor to CPU asynchronously.

Parameters

stream (hidet.cuda.Stream, optional) – The stream to copy the tensor to CPU on.

Returns

ret – The tensor on CPU.

Return type

Tensor

cuda_async(device=None, stream=None)[source]

Copy the tensor to GPU asynchronously.

Parameters

  • device (Device, optional) – The target cuda device. None indicates the current cuda device.

  • stream (hidet.cuda.Stream, optional) – The stream to copy the tensor to GPU on. None indicates the current stream.

Returns

ret – The tensor on GPU.

Return type

Tensor

copy_async(stream=None)[source]

Create a copy of current tensor asynchronously.

Parameters

stream (hidet.cuda.Stream, optional) – The stream to copy the tensor. None indicates the current stream of the device where self tensor is on.

Returns

ret – A new tensor with the same contents as the current one.

Return type

Tensor

detach()[source]

Detach the current tensor from tracing.

Returns

ret – The detached tensor.

Return type

Tensor

numpy()[source]

Convert the tensor to a numpy array.

The tensor must be on CPU device. Otherwise, a RuntimeError will be raised. The returned numpy array will share the same memory with the tensor.

Returns

ret – The numpy array.

Return type

np.ndarray

torch()[source]

Convert to a torch tensor.

Returns

ret – The torch tensor that shares the memory with the hidet tensor.

Return type

torch.Tensor

to(dtype=None, device=None)[source]

Cast the data type of current tensor or/and move it to another device.

Parameters

  • dtype (DataType or str, optional) – The target data type to convert to. None indicates unchanged.

  • device (Device or str, optional) – The target device to copy the tensor. None indicates unchanged.

Returns

ret – The tensor with the new data type on target device.

Return type

Tensor

item()[source]

Convert the tensor to a scalar value.

Return type

Union[int, float, bool]

signature()[source]

Get the signature of the tensor.

Returns

ret – The signature of the tensor.

Return type

str

is_symbolic()[source]

Check if the tensor is symbolic.

A tensor is symbolic if it is not backed by any storage (i.e., self.storage is None).

Returns

ret – True if the tensor is symbolic, False otherwise.

Return type

bool

contiguous()[source]

Create a tensor with contiguous row-major layout.

If the tensor already has the continuous row-major layout, this tensor is returned directly.

Returns

ret – The tensor with contiguous row-major layout.

Return type

Tensor

reshape(shape)[source]

Create a reshaped tensor.

See Also hidet.graph.ops.reshape().

Parameters

shape (Sequence[int]) – The new shape.

Returns

ret – The reshaped tensor.

Return type

Tensor

squeeze(dims)[source]

Create a squeezed tensor.

See Also hidet.graph.ops.squeeze().

Parameters

dims (Union[int, Sequence[int]]) – The dimension(s) to squeeze.

Returns

ret – The squeezed tensor.

Return type

Tensor

unsqueeze(dims)[source]

Create a unsqueezed tensor.

See Also hidet.graph.ops.unsqueeze().

Parameters

dims (Union[int, Sequence[int]]) – The dimensions to unsqueeze.

Returns

ret – The unsqueezed tensor.

Return type

Tensor

rearrange(plan)[source]

Create a rearranged tensor.

See Also hidet.graph.ops.rearrange().

Parameters

plan (List[List[int]]) – The rearrange plan.

Returns

ret – The rearranged tensor.

Return type

Tensor

sum(dims, keep_dim=False)[source]

Create a sum reduced tensor.

See Also hidet.graph.ops.reduce_sum().

Parameters

  • dims (Union[int, List[int]]) – The dimensions to sum up.

  • keep_dim (bool) – Whether to keep the reduced dimensions.

Returns

ret – The reduced tensor.

Return type

Tensor

mean(dims, keep_dim=False)[source]

Create a mean reduced tensor.

See Also hidet.graph.ops.reduce_mean().

Parameters

  • dims (Union[int, List[int]]) – The dimensions to average up.

  • keep_dim (bool) – Whether to keep the reduced dimensions.

Returns

ret – The reduced tensor.

Return type

Tensor