---

id: version-2.0.0-tensor title: Tensor original_id: 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 Usage

Create Tensor

>>> import numpy as np
>>> from singa import tensor
>>> tensor.from_numpy( np.asarray([[1, 0, 0], [0, 1, 0]], dtype=np.float32) )
[[1. 0. 0.]
 [0. 1. 0.]]

Convert to numpy

>>> a = np.asarray([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
>>> tensor.from_numpy(a)
[[1. 0. 0.]
 [0. 1. 0.]]
>>> tensor.to_numpy(tensor.from_numpy(a))
array([[1., 0., 0.],
       [0., 1., 0.]], dtype=float32)

Tensor Methods

>>> t = tensor.from_numpy(a)
>>> t.transpose([1,0])
[[1. 0.]
 [0. 1.]
 [0. 0.]]

Tensor Arithmetic Methods

tensor is evaluated in real time.

>>> t + 1
[[2. 1. 1.]
 [1. 2. 1.]]
>>> t / 5
[[0.2 0.  0. ]
 [0.  0.2 0. ]]

Tensor Functions

Functions in module singa.tensor return new tensor object after applying defined transformation in the function.

>>> tensor.log(t+1)
[[0.6931472 0.        0.       ]
 [0.        0.6931472 0.       ]]

Tensor on Different Devices

tensor is created on host(CPU) by default, and can also be created on different backends by specifiying the device. Existing tensor could also be moved between device by to_device().

>>> from singa import device
>>> x = tensor.Tensor((2, 3), device.create_cuda_gpu())
>>> x.gaussian(1,1)
>>> x
[[1.531889   1.0128608  0.12691343]
 [2.1674204  3.083676   2.7421203 ]]
>>> # move to host
>>> x.to_device(device.get_default_device())

Simple Neural Network Example

from singa import device
from singa import tensor
from singa import opt
from singa import autograd
class MLP:
    def __init__(self):
        self.linear1 = autograd.Linear(3, 4)
        self.linear2 = autograd.Linear(4, 5)
    def forward(self, x):
        y=self.linear1(x)
        return self.linear2(y)
def train(model, x, t, dev, epochs=10):
    for i in range(epochs):
        y = model.forward(x)
        loss = autograd.mse_loss(y, t)
        print("loss: ", loss)
        sgd = opt.SGD()
        for p, gp in autograd.backward(loss):
            sgd.update(p, gp)
        sgd.step()
    print("training completed")
if __name__ == "__main__":
    autograd.training = True
    model = MLP()
    dev = device.get_default_device()
    x = tensor.Tensor((2, 3), dev)
    t = tensor.Tensor((2, 5), dev)
    x.gaussian(1,1)
    t.gaussian(1,1)
    train(model, x, t, dev)

Output:

loss:  [4.917431]
loss:  [2.5147934]
loss:  [2.0670078]
loss:  [1.9179827]
loss:  [1.8192691]
loss:  [1.7269677]
loss:  [1.6308627]
loss:  [1.52674]
loss:  [1.4122975]
loss:  [1.2866782]
training completed

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

work in progress

CPP API

work in progress