Tensor

Each Tensor instance is a multi-dimensional array allocated on a specific Device instance. Tensor instances store variables and provide linear algebra operations over different types of hardware devices without user awareness. Note that users need to make sure the tensor operands are allocated on the same device except copy functions.

Tensor implementation

SINGA has three different sets of implmentations of Tensor functions, one for each type of Device.

  • ‘tensor_math_cpp.h’ implements operations using Cpp (with CBLAS) for CppGPU devices.
  • ‘tensor_math_cuda.h’ implements operations using Cuda (with cuBLAS) for CudaGPU devices.
  • ‘tensor_math_opencl.h’ implements operations using OpenCL for OpenclGPU devices.

Python API

Example usage:

import numpy as np
from singa import tensor
from singa import device

# create a tensor with shape (2,3), default CppCPU device and float32
x = tensor.Tensor((2, 3))
x.set_value(0.4)

# create a tensor from a numpy array
npy = np.zeros((3, 3), dtype=np.float32)
y = tensor.from_numpy(npy)

y.uniform(-1, 1)  # sample values from the uniform distribution

z = tensor.mult(x, y)  # gemm -> z of shape (2, 3)

x += z  # element-wise addition

dev = device.get_default_device()
x.to_device(dev)  # move the data to a gpu device

r = tensor.relu(x)

s = tensor.to_numpy(r)  # tensor -> numpy array

There are two sets of tensor functions,

Tensor member functions
which would change the internal state of the Tensor instance.
Tensor module functions
which accept Tensor instances as arguments and return Tensor instances.

Every Tesor instance must be initialized before reading data from it.

class singa.tensor.Tensor(shape=None, device=None, dtype=0)

Create a Py Tensor, which wraps a swig converted Tensor from CPP Tensor

The three arguments are three attributes of the Tensor.

Parameters:
  • shape (list<int>) – a list of integers for the tensor shape. If shape is not specified, the created tensor is called a dummy tensor.
  • device – a swig converted Device instance using the device moduel . If it is None, then the default host device would be used.
  • dtype – data type. currently, most operations only accept kFloat32.
T()

shallow copy, negate the transpose field.

Returns:a new Tensor which shares the underlying data memory (shallow copy) but is marked as a transposed version of this tensor.
add_column(v)

Add a tensor to each column of this tensor.

Parameters:v (Tensor) – a Tensor to be added as a column to this tensor.
add_row(v)

Add a tensor to each row of this tensor.

Parameters:v (Tensor) – a Tensor to be added as a row to this tensor.
bernoulli(p)

Sample 0/1 for each element according to the given probability.

Parameters:p (float) – with probability p, each element is sample to 1.
clone()
Returns:a new Tensor which does deep copy of this tensor
copy()

shallow copy calls copy constructor of singa::Tensor

copy_data(t)

Copy data from other Tensor instance.

Parameters:t (Tensor) – source Tensor.
copy_from_numpy(np_array, offset=0)

Copy the data from the numpy array.

Parameters:
  • np_array – source numpy array
  • offset (int) – destination offset
deepcopy()

Same as clone().

Returns:a new Tensor
div_column(v)

Divide each column of this tensor by v.

Parameters:v (Tensor) – 1d tensor of the same length the column of self.
div_row(v)

Divide each row of this tensor by v.

Parameters:v (Tensor) – 1d tensor of the same length the row of self.
gaussian(mean, std)

Generate a value for each element following a Gaussian distribution.

Parameters:
  • mean (float) – mean of the distribution
  • std (float) – standard variance of the distribution
is_empty()
Returns:True if the tensor is empty according to its shape
is_transpose()
Returns:True if the internal data is transposed; otherwise False.
l1()
Returns:the L1 norm.
l2()
Returns:the L2 norm.
memsize()
Returns:the number of Bytes allocated for this tensor.
mult_column(v)

Multiply each column of this tensor by v element-wisely.

Parameters:v (Tensor) – 1d tensor of the same length the column of self.
mult_row(v)

Multiply each row of this tensor by v element-wisely.

Parameters:v (Tensor) – 1d tensor of the same length the row of self.
ndim()
Returns:the number of dimensions of the tensor.
reset_like(t)

Reset the shape, dtype and device as the given tensor.

Parameters:t (Tensor) –
reshape(shape)

Change the tensor shape.

Parameters:shape (list<int>) – new shape, which should have the same volumn as the original shape.
set_value(x)

Set all elements of the tensor to be the give value.

Parameters:x (float) –
size()
Returns:the number of elements of the tensor.
to_device(device)

Move the tensor data onto a given device.

Parameters:device – a swig Device converted from CudaGPU or CppCPU or OpenclGPU
to_host()

Move the tensor data onto the default host CppCPU device.

uniform(low, high)

Generate a value for each element following a uniform distribution.

Parameters:
  • low (float) – the lower bound
  • high (float) – the hight bound
singa.tensor.abs(t)
Parameters:t (Tensor) – input Tensor
Returns:a new Tensor whose element y = abs(x), x is an element of t
singa.tensor.add(lhs, rhs, ret=None)

Elementi-wise addition.

Parameters:
  • lhs (Tensor) –
  • rhs (Tensor) –
  • ret (Tensor, optional) – if not None, the result is stored in it; otherwise, a new Tensor would be created for the result.
Returns:

the result Tensor

singa.tensor.add_column(alpha, v, beta, M)

Add v to each column of M.

Denote each column of M as m, m = alpha * v + beta * m

Parameters:
  • alpha (float) –
  • v (Tensor) –
  • beta (float) –
  • M (Tensor) – 2d tensor
Returns:

M

singa.tensor.add_row(alpha, v, beta, M)

Add v to each row of M.

Denote each row of M as m, m = alpha * v + beta * m

Parameters:
  • alpha (float) –
  • v (Tensor) –
  • beta (float) –
  • M (Tensor) – 2d tensor
Returns:

M

singa.tensor.average(t, axis=None)
Parameters:
  • t (Tensor) – input Tensor
  • axis (int, optional) – if None, average all elements; otherwise average along the given dimension. 0 for averaging each column; 1 for averaging each row.
Returns:

a float value if axis is None; otherwise, a new Tensor for the result.

singa.tensor.axpy(alpha, x, y)

Element-wise operation for y += alpha * x.

Parameters:
Returns:

y

singa.tensor.bernoulli(p, t)

Generate a binary value for each element of t.

Parameters:
  • p (float) – each element is 1 with probability p; and 0 with 1 - p
  • t (Tensor) – the results are put into t
Returns:

t

singa.tensor.copy_data_to_from(dst, src, size, dst_offset=0, src_offset=0)

Copy the data between two Tensor instances which could be on different devices.

Parameters:
  • dst (Tensor) – destination Tensor
  • src (Tensor) – source Tensor
  • size (int) – number of elements to copy
  • dst_offset (int) – offset in terms of elements to the start of dst
  • src_offset (int) – offset in terms of elements to the start of src
singa.tensor.div(lhs, rhs, ret=None)

Elementi-wise division.

Parameters:
  • lhs (Tensor) –
  • rhs (Tensor) –
  • ret (Tensor, optional) – if not None, the result is stored in it; otherwise, a new Tensor would be created for the result.
Returns:

the result Tensor

singa.tensor.eltwise_mult(lhs, rhs, ret=None)

Elementi-wise multiplication.

Parameters:
  • lhs (Tensor) –
  • rhs (Tensor) –
  • ret (Tensor, optional) – if not None, the result is stored in it; otherwise, a new Tensor would be created for the result.
Returns:

the result Tensor

singa.tensor.exp(t)
Parameters:t (Tensor) – input Tensor
Returns:a new Tensor whose element y = exp(x), x is an element of t
singa.tensor.from_numpy(np_array)

Create a Tensor instance with the shape, dtype and values from the numpy array.

Parameters:np_array – the numpy array.
Returns:A Tensor instance allocated on the default CppCPU device.
singa.tensor.gaussian(mean, std, t)

Generate values following a Gaussian distribution.

Parameters:
  • mean (float) – the mean of the Gaussian distribution.
  • std (float) – the standard variance of the Gaussian distribution.
  • t (Tensor) – the results are put into t
Returns:

t

singa.tensor.ge(t, x)

Elementi-wise comparison for t >= x.

Parameters:
  • t (Tensor) – left hand side operand
  • x (Tensor or float) – right hand side operand
Returns:

0.0f, or t[i] >= x[i] ? 1.0f:0.0f

Return type:

a Tensor with each element being t[i] >= x ? 1.0f

singa.tensor.gt(t, x)

Elementi-wise comparison for t > x.

Parameters:
  • t (Tensor) – left hand side operand
  • x (Tensor or float) – right hand side operand
Returns:

0.0f, or t[i] > x[i] ? 1.0f:0.0f

Return type:

a Tensor with each element being t[i] > x ? 1.0f

singa.tensor.le(t, x)

Elementi-wise comparison for t <= x.

Parameters:
  • t (Tensor) – left hand side operand
  • x (Tensor or float) – right hand side operand
Returns:

0.0f, or t[i] <= x[i] ? 1.0f:0.0f

Return type:

a Tensor with each element being t[i] <= x ? 1.0f

singa.tensor.log(t)
Parameters:t (Tensor) – input Tensor
Returns:a new Tensor whose element y = log(x), x is an element of t
singa.tensor.lt(t, x)

Elementi-wise comparison for t < x

Parameters:
  • t (Tensor) – left hand side operand
  • x (Tensor or float) – right hand side operand
Returns:

0.0f, or t[i] < x[i] ? 1.0f:0.0f

Return type:

a Tensor with each element being t[i] < x ? 1.0f

singa.tensor.mult(A, B, C=None, alpha=1.0, beta=0.0)

Do matrix-matrix or matrix-vector multiplication.

This function returns C = alpha * A * B + beta * C

Parameters:
  • A (Tensor) – 2d Tensor
  • B (Tensor) – If B is a 1d Tensor, GEMV would be invoked for matrix-vector multiplication; otherwise GEMM would be invoked.
  • C (Tensor, optional) – for storing the result; If None, a new Tensor would be created.
  • alpha (float) –
  • beta (float) –
Returns:

the result Tensor

singa.tensor.pow(t, x, out=None)
Parameters:
  • t (Tensor) – input tensor
  • x (float or Tensor) – y[i] = t[i]^x if x is a float value; otherwise, y[i]= t[i]^x[i] if x is a tensor.
  • out (None or Tensor) – if None, a new Tensor would be constructed to store the result; otherwise, the result is put into out.
Returns:

the result tensor.

singa.tensor.relu(t)
Parameters:t (Tensor) – input Tensor
Returns:a new Tensor whose element y = x if x >0; otherwise 0; x is an element of t
singa.tensor.reshape(t, s)

Reshape the input tensor with the given shape.

Parameters:
  • t (Tensor) – the tensor to be changed
  • s (list<int>) – the new shape, which should have the same volumn as the old shape.
Returns:

the new Tensor

singa.tensor.sigmoid(t)
Parameters:t (Tensor) – input Tensor
Returns:a new Tensor whose element y = sigmoid(x); x is an element of t
singa.tensor.sign(t)
Parameters:t (Tensor) – input Tensor
Returns:a new Tensor whose element y = sign(x)
singa.tensor.sizeof(dtype)
Returns:the number of bytes of the given SINGA data type defined in core.proto
singa.tensor.softmax(t, out=None)

Apply SoftMax for each row of the Tensor.

Parameters:
  • t (Tensor) – the input 1d or 2d tensor
  • out (Tensor, optional) – if not None, it is used to store the result
Returns:

the result Tensor

singa.tensor.sqrt(t)
Parameters:t (Tensor) – input Tensor
Returns:a new Tensor whose element y = sqrt(x), x is an element of t
singa.tensor.square(t)
Parameters:t (Tensor) – input Tensor
Returns:a new Tensor whose element y = x * x, x is an element of t
singa.tensor.sub(lhs, rhs, ret=None)

Elementi-wise subtraction.

Parameters:
  • lhs (Tensor) –
  • rhs (Tensor) –
  • ret (Tensor, optional) – if not None, the result is stored in it; otherwise, a new Tensor would be created for the result.
Returns:

the result Tensor

singa.tensor.sum(t, axis=None)

Sum elements of the input tensor long the given axis.

Parameters:
  • t (Tensor) – input Tensor
  • axis (int, optional) – if None, the summation is done over all elements; if axis is provided, then it is calculated along the given axis, e.g. 0 – sum each column; 1 – sum each row.
Returns:

a float value as the sum of all elements, or a new Tensor

singa.tensor.sum_columns(M)

Sum all columns into a single column.

Parameters:M (Tensor) – the input 2d tensor.
Returns:a new Tensor as the resulted column.
singa.tensor.sum_rows(M)

Sum all rows into a single row.

Parameters:M (Tensor) – the input 2d tensor.
Returns:a new Tensor as the resulted row.
singa.tensor.tanh(t)
Parameters:t (Tensor) – input Tensor
Returns:a new Tensor whose element y = tanh(x), x is an element of t
singa.tensor.to_host(t)

Copy the data to a host tensor.

singa.tensor.to_numpy(t)

Copy the tensor into a numpy array.

Parameters:t (Tensor) –
Returns:a numpy array
singa.tensor.uniform(low, high, t)

Generate values following a Uniform distribution.

Parameters:
  • low (float) – the lower bound
  • hight (float) – the higher bound
  • t (Tensor) – the results are put into t
Returns:

t

CPP API