NumPy Interfaces in MindSpore

Ascend GPU CPU Model Development

Overview

MindSpore Numpy package contains a set of Numpy-like interfaces, which allows developers to build models on MindSpore with similar syntax of Numpy.

This sample is suitable for CPU, GPU and Ascend environments.

Operator Functions

Mindspore Numpy operators can be classified into four functional modules: array generation, array operation, logic operation and math operation. For details about the supported operators on the Ascend AI processors, GPU, and CPU, see Numpy Interface List.

Array Generations

Array generation operators are used to generate tensors.

Here is an example to generate an array:

[1]:

import mindspore.numpy as np
import mindspore.ops as ops
input_x = np.array([1, 2, 3], np.float32)
print("input_x =", input_x)
print("type of input_x =", ops.typeof(input_x))

input_x = [1. 2. 3.]
type of input_x = Tensor[Float32]

Here we have more examples:

Generate a tensor filled with the same element

np.full can be used to generate a tensor with user-specified values:

[2]:

input_x = np.full((2, 3), 6, np.float32)
print(input_x)

[[6. 6. 6.]
 [6. 6. 6.]]

Here is another example to generate an array with the specified shape and filled with the value of 1:

[3]:

input_x = np.ones((2, 3), np.float32)
print(input_x)

[[1. 1. 1.]
 [1. 1. 1.]]

Generate tensors in a specified range

Generate an arithmetic array within the specified range：

[4]:

input_x = np.arange(0, 5, 1)
print(input_x)

[0 1 2 3 4]

Generate tensors with specific requirement

Generate a matrix where the lower elements are 1 and the upper elements are 0 on the given diagonal:

[5]:

input_x = np.tri(3, 3, 1)
print(input_x)

[[1. 1. 0.]
 [1. 1. 1.]
 [1. 1. 1.]]

Another example, generate a 2-D matrix with a diagonal of 1 and other elements of 0:

[6]:

input_x = np.eye(2, 2)
print(input_x)

[[1. 0.]
 [0. 1.]]

Array Operations

Array operations focus on tensor manipulation.

Manipulate the shape of the tensor

For example, transpose a matrix:

[7]:

input_x = np.arange(10).reshape(5, 2)
output = np.transpose(input_x)
print(output)

[[0 2 4 6 8]
 [1 3 5 7 9]]

Another example, swap two axes:

[8]:

input_x = np.ones((1, 2, 3))
output = np.swapaxes(input_x, 0, 1)
print(output.shape)

(2, 1, 3)

Tensor splitting

Divide the input tensor into multiple tensors equally, for example:

[9]:

input_x = np.arange(9)
output = np.split(input_x, 3)
print(output)

(Tensor(shape=[3], dtype=Int32, value= [0, 1, 2]), Tensor(shape=[3], dtype=Int32, value= [3, 4, 5]), Tensor(shape=[3], dtype=Int32, value= [6, 7, 8]))

Tensor combination

Concatenate the two tensors according to the specified axis, for example:

[10]:

input_x = np.arange(0, 5)
input_y = np.arange(10, 15)
output = np.concatenate((input_x, input_y), axis=0)
print(output)

[ 0  1  2  3  4 10 11 12 13 14]

Logic Operations

Logic operations define computations related with boolean types. Examples of equal and less operations are as follows:

[11]:

input_x = np.arange(0, 5)
input_y = np.arange(0, 10, 2)
output = np.equal(input_x, input_y)
print("output of equal:", output)
output = np.less(input_x, input_y)
print("output of less:", output)

output of equal: [ True False False False False]
output of less: [False  True  True  True  True]

Math Operations

Math operations include basic and advanced math operations on tensors, and they have full support on Numpy broadcasting rules. Here are some examples:

Sum two tensors

The following code implements the operation of adding two tensors of input_x and input_y:

[12]:

input_x = np.full((3, 2), [1, 2])
input_y = np.full((3, 2), [3, 4])
output = np.add(input_x, input_y)
print(output)

[[4 6]
 [4 6]
 [4 6]]

Matrics multiplication

The following code implements the operation of multiplying two matrices input_x and input_y:

[13]:

input_x = np.arange(2*3).reshape(2, 3).astype('float32')
input_y = np.arange(3*4).reshape(3, 4).astype('float32')
output = np.matmul(input_x, input_y)
print(output)

[[20. 23. 26. 29.]
 [56. 68. 80. 92.]]

Take the average along a given axis

The following code implements the operation of averaging all the elements of input_x:

[14]:

input_x = np.arange(6).astype('float32')
output = np.mean(input_x)
print(output)

2.5

Exponential arithmetic

The following code implements the operation of the natural constant e to the power of input_x:

[15]:

input_x = np.arange(5).astype('float32')
output = np.exp(input_x)
print(output)

[ 1.         2.7182817  7.389056  20.085537  54.59815  ]

Interact With MindSpore Functions

Since mindspore.numpy directly wraps MindSpore tensors and operators, it has all the advantages and properties of MindSpore. In this section, we will briefly introduce how to employ MindSpore execution management and automatic differentiation in mindspore.numpy coding scenarios. These include:

ms_function: for running codes in static graph mode for better efficiency.
GradOperation: for automatic gradient computation.
mindspore.context: for mindspore.numpy execution management.
mindspore.nn.Cell: for using mindspore.numpy interfaces in MindSpore Deep Learning Models.

Use ms_function to run code in static graph mode

Let’s first see an example consisted of matrix multiplication and bias add, which is a typical process in Neural Networks:

[16]:

import mindspore.numpy as np

x = np.arange(8).reshape(2, 4).astype('float32')
w1 = np.ones((4, 8))
b1 = np.zeros((8,))
w2 = np.ones((8, 16))
b2 = np.zeros((16,))
w3 = np.ones((16, 4))
b3 = np.zeros((4,))

def forward(x, w1, b1, w2, b2, w3, b3):
    x = np.dot(x, w1) + b1
    x = np.dot(x, w2) + b2
    x = np.dot(x, w3) + b3
    return x

print(forward(x, w1, b1, w2, b2, w3, b3))

[[ 768.  768.  768.  768.]
 [2816. 2816. 2816. 2816.]]

In this function, MindSpore dispatches each computing kernel to device separately. However, with the help of ms_function, we can compile all operations into a single static computing graph.

[17]:

from mindspore import ms_function

forward_compiled = ms_function(forward)
print(forward(x, w1, b1, w2, b2, w3, b3))

[[ 768.  768.  768.  768.]
 [2816. 2816. 2816. 2816.]]

Currently, static graph cannot run in Python interactive mode and not all python types can be passed into functions decorated with ms_function. For details about how to use ms_function, see API: ms_function.

Use GradOperation to compute deratives

GradOperation can be used to take deratives from normal functions and functions decorated with ms_function. Take the previous example:

[18]:

from mindspore import ops

grad_all = ops.composite.GradOperation(get_all=True)
grad_all(forward)(x, w1, b1, w2, b2, w3, b3)

[18]:

(Tensor(shape=[2, 4], dtype=Float32, value=
 [[ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02,  5.12000000e+02],
  [ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02,  5.12000000e+02]]),
 Tensor(shape=[4, 8], dtype=Float32, value=
 [[ 2.56000000e+02,  2.56000000e+02,  2.56000000e+02 ...  2.56000000e+02,  2.56000000e+02,  2.56000000e+02],
  [ 3.84000000e+02,  3.84000000e+02,  3.84000000e+02 ...  3.84000000e+02,  3.84000000e+02,  3.84000000e+02],
  [ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02 ...  5.12000000e+02,  5.12000000e+02,  5.12000000e+02]
  [ 6.40000000e+02,  6.40000000e+02,  6.40000000e+02 ...  6.40000000e+02,  6.40000000e+02,  6.40000000e+02]]),
  ...
 Tensor(shape=[4], dtype=Float32, value= [ 2.00000000e+00,  2.00000000e+00,  2.00000000e+00,  2.00000000e+00]))

To take the gradient of ms_function compiled functions, first we need to set the execution mode to static graph mode.

[19]:

from mindspore import ms_function, context

context.set_context(mode=context.GRAPH_MODE)
grad_all = ops.composite.GradOperation(get_all=True)
grad_all(ms_function(forward))(x, w1, b1, w2, b2, w3, b3)

[19]:

(Tensor(shape=[2, 4], dtype=Float32, value=
 [[ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02,  5.12000000e+02],
  [ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02,  5.12000000e+02]]),
 Tensor(shape=[4, 8], dtype=Float32, value=
 [[ 2.56000000e+02,  2.56000000e+02,  2.56000000e+02 ...  2.56000000e+02,  2.56000000e+02,  2.56000000e+02],
  [ 3.84000000e+02,  3.84000000e+02,  3.84000000e+02 ...  3.84000000e+02,  3.84000000e+02,  3.84000000e+02],
  [ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02 ...  5.12000000e+02,  5.12000000e+02,  5.12000000e+02]
  [ 6.40000000e+02,  6.40000000e+02,  6.40000000e+02 ...  6.40000000e+02,  6.40000000e+02,  6.40000000e+02]]),
  ...
 Tensor(shape=[4], dtype=Float32, value= [ 2.00000000e+00,  2.00000000e+00,  2.00000000e+00,  2.00000000e+00]))

For more details, see API: GradOperation.

Use mindspore.context to control execution mode

Most functions in mindspore.numpy can run in Graph Mode and PyNative Mode, and can run on CPU，GPU and Ascend. Like MindSpore, users can manage the execution mode using mindspore.context：

from mindspore import context

# Execucation in static graph mode
context.set_context(mode=context.GRAPH_MODE)

# Execucation in PyNative mode
context.set_context(mode=context.PYNATIVE_MODE)

# Execucation on CPU backend
context.set_context(device_target="CPU")

# Execucation on GPU backend
context.set_context(device_target="GPU")

# Execucation on Ascend backend
context.set_context(device_target="Ascend")
...

For more details, see API: mindspore.context.

Use mindspore.numpy in MindSpore Deep Learning Models

mindspore.numpy interfaces can be used inside nn.cell blocks as well. For example, the above code can be modified to:

[20]:

import mindspore.numpy as np
from mindspore import context
from mindspore.nn import Cell

context.set_context(mode=context.GRAPH_MODE)

x = np.arange(8).reshape(2, 4).astype('float32')
w1 = np.ones((4, 8))
b1 = np.zeros((8,))
w2 = np.ones((8, 16))
b2 = np.zeros((16,))
w3 = np.ones((16, 4))
b3 = np.zeros((4,))

class NeuralNetwork(Cell):
    def __init__(self):
        super(NeuralNetwork, self).__init__()

    def construct(self, x, w1, b1, w2, b2, w3, b3):
        x = np.dot(x, w1) + b1
        x = np.dot(x, w2) + b2
        x = np.dot(x, w3) + b3
        return x

net = NeuralNetwork()

print(net(x, w1, b1, w2, b2, w3, b3))

[[ 768.  768.  768.  768.]
 [2816. 2816. 2816. 2816.]]

For more details on building Neural Network with MindSpore, see MindSpore Training Guide.