Cell and Parameter
Cell, as the basic unit of neural network construction, corresponds to the concept of neural network layer, and the abstract encapsulation of Tensor computation operation can represent the neural network structure more accurately and clearly. In addition to the basic Tensor computation flow definition, the neural network layer contains functions such as parameter management and state management. Parameter is the core of neural network training and is usually used as an internal member variable of the neural network layer. In this section, we systematically introduce parameters, neural network layers and their related usage.
Parameter
Parameter is a special class of Tensor, which is a variable whose value can be updated during model training. MindSpore provides the mindspore.Parameter class for Parameter construction. In order to distinguish between Parameter for different purposes, two different categories of Parameter are defined below. In order to distinguish between Parameter for different purposes, two different categories of Parameter are defined below:
Trainable parameter. Tensor that is updated after the gradient is obtained according to the backward propagation algorithm during model training, and
required_gradneeds to be set toTrue.Untrainable parameters. Tensor that does not participate in backward propagation needs to update values (e.g.
meanandvarvariables in BatchNorm), whenrequires_gradneeds to be set toFalse.
Parameter is set to
required_grad=Trueby default.
We construct a simple fully-connected layer as follows:
import numpy as np
import mindspore
from mindspore import nn
from mindspore import ops
from mindspore import Tensor, Parameter
class Network(nn.Cell):
def __init__(self):
super().__init__()
self.w = Parameter(Tensor(np.random.randn(5, 3), mindspore.float32), name='w') # weight
self.b = Parameter(Tensor(np.random.randn(3,), mindspore.float32), name='b') # bias
def construct(self, x):
z = ops.matmul(x, self.w) + self.b
return z
net = Network()
In the __init__ method of Cell, we define two parameters w and b and configure name for namespace management. Use self.attr in the construct method to call directly to participate in Tensor operations.
Obtaining Parameter
After constructing the neural network layer by using Cell+Parameter, we can use various methods to obtain the Parameter managed by Cell.
Obtaining a Single Parameter
To get a particular parameter individually, just call a member variable of a Python class directly.
print(net.b.asnumpy())
[-1.2192779 -0.36789745 0.0946381 ]
Obtaining a Trainable Parameter
Trainable parameters can be obtained by using the Cell.trainable_params method, and this interface is usually called when configuring the optimizer.
print(net.trainable_params())
[Parameter (name=w, shape=(5, 3), dtype=Float32, requires_grad=True), Parameter (name=b, shape=(3,), dtype=Float32, requires_grad=True)]
Obtaining All Parameters
Use the Cell.get_parameters() method to get all parameters, at which point a Python iterator will be returned.
print(type(net.get_parameters()))
<class 'generator'>
Or you can call Cell.parameters_and_names to return the parameter names and parameters.
for name, param in net.parameters_and_names():
print(f"{name}:\n{param.asnumpy()}")
w:
[[ 4.15680408e-02 -1.20311625e-01 5.02573885e-02]
[ 1.22175144e-04 -1.34980649e-01 1.17642188e+00]
[ 7.57667869e-02 -1.74758151e-01 -5.19092619e-01]
[-1.67846107e+00 3.27240258e-01 -2.06452996e-01]
[ 5.72323874e-02 -8.27963874e-02 5.94243526e-01]]
b:
[-1.2192779 -0.36789745 0.0946381 ]
Modifying the Parameter
Modifying Parameter Values Directly
Parameter is a special kind of Tensor, so its value can be modified by using the Tensor index modification.
net.b[0] = 1.
print(net.b.asnumpy())
[ 1. -0.36789745 0.0946381 ]
Overriding the Modified Parameter Values
The Parameter.set_data method can be called to override the Parameter by using a Tensor with the same Shape. This method is commonly used for Cell traversal initialization by using Initializer.
net.b.set_data(Tensor([3, 4, 5]))
print(net.b.asnumpy())
[3. 4. 5.]
Modifying Parameter Values During Runtime
The main role of parameters is to update their values during model training, which involves parameter modification during runtime after backward propagation to obtain gradients, or when untrainable parameters need to be updated. Due to the compiled design of MindSpore’s Accelerating with Static Graphs, it is necessary at this point to use the mindspore.ops.assign interface to assign parameters. This method is commonly used in Custom Optimizer scenarios. The following is a simple sample modification of parameter values during runtime:
import mindspore as ms
@ms.jit
def modify_parameter():
b_hat = ms.Tensor([7, 8, 9])
ops.assign(net.b, b_hat)
return True
modify_parameter()
print(net.b.asnumpy())
[7. 8. 9.]
Parameter Tuple
ParameterTuple, variable tuple, used to store multiple Parameter, is inherited from tuple tuples, and provides cloning function.
The following example provides the ParameterTuple creation method:
from mindspore.common.initializer import initializer
from mindspore import ParameterTuple
# Creation
x = Parameter(default_input=ms.Tensor(np.arange(2 * 3).reshape((2, 3))), name="x")
y = Parameter(default_input=initializer('ones', [1, 2, 3], ms.float32), name='y')
z = Parameter(default_input=2.0, name='z')
params = ParameterTuple((x, y, z))
# Clone from params and change the name to "params_copy"
params_copy = params.clone("params_copy")
print(params)
print(params_copy)
(Parameter (name=x, shape=(2, 3), dtype=Int64, requires_grad=True), Parameter (name=y, shape=(1, 2, 3), dtype=Float32, requires_grad=True), Parameter (name=z, shape=(), dtype=Float32, requires_grad=True))
(Parameter (name=params_copy.x, shape=(2, 3), dtype=Int64, requires_grad=True), Parameter (name=params_copy.y, shape=(1, 2, 3), dtype=Float32, requires_grad=True), Parameter (name=params_copy.z, shape=(), dtype=Float32, requires_grad=True))
Cell Training State Change
Some Tensor operations in neural networks do not behave the same during training and inference, e.g., nn.Dropout performs random dropout during training but not during inference, and nn.BatchNorm requires updating the mean and var variables during training and fixing their values unchanged during inference. So we can set the state of the neural network through the Cell.set_train interface.
When set_train is set to True, the neural network state is train, and the default value of set_train interface is True:
net.set_train()
print(net.phase)
train
When set_train is set to False, the neural network state is predict:
net.set_train(False)
print(net.phase)
predict
Custom Neural Network Layers
Normally, the neural network layer interface and function interface provided by MindSpore can meet the model construction requirements, but since the AI field is constantly updating, it is possible to encounter new network structures without built-in modules. At this point, we can customize the neural network layer through the function interface provided by MindSpore, Primitive operator, and can use the Cell.bprop method to customize the reverse. The following are the details of each of the three customization methods.
Constructing Neural Network Layers by Using the Function Interface
MindSpore provides a large number of basic function interfaces, which can be used to construct complex Tensor operations, encapsulated as neural network layers. The following is an example of Threshold with the following equation:
It can be seen that Threshold determines whether the value of the Tensor is greater than the threshold value, keeps the value whose judgment result is True, and replaces the value whose judgment result is False. Therefore, the corresponding implementation is as follows:
class Threshold(nn.Cell):
def __init__(self, threshold, value):
super().__init__()
self.threshold = threshold
self.value = value
def construct(self, inputs):
cond = ops.gt(inputs, self.threshold)
value = ops.fill(inputs.dtype, inputs.shape, self.value)
return ops.select(cond, inputs, value)
Here ops.gt, ops.fill, and ops.select are used to implement judgment and replacement respectively. The following custom Threshold layer is implemented:
m = Threshold(0.1, 20)
inputs = mindspore.Tensor([0.1, 0.2, 0.3], mindspore.float32)
m(inputs)
Tensor(shape=[3], dtype=Float32, value= [ 2.00000000e+01, 2.00000003e-01, 3.00000012e-01])
It can be seen that inputs[0] = threshold, so it is replaced with 20.
Custom Cell Reverse
In special scenarios, we not only need to customize the forward logic of the neural network layer, but also want to manually control the computation of its reverse, which we can define through the Cell.bprop interface. The function will be used in scenarios such as new neural network structure design and backward propagation speed optimization. In the following, we take Dropout2d as an example to introduce custom Cell reverse.
class Dropout2d(nn.Cell):
def __init__(self, keep_prob):
super().__init__()
self.keep_prob = keep_prob
self.dropout2d = ops.Dropout2D(keep_prob)
def construct(self, x):
return self.dropout2d(x)
def bprop(self, x, out, dout):
_, mask = out
dy, _ = dout
if self.keep_prob != 0:
dy = dy * (1 / self.keep_prob)
dy = mask.astype(mindspore.float32) * dy
return (dy.astype(x.dtype), )
dropout_2d = Dropout2d(0.8)
dropout_2d.bprop_debug = True
The bprop method has three separate input parameters:
x: Forward input. When there are multiple forward inputs, the same number of inputs are required.
out: Forward input.
dout: When backward propagation is performed, the current Cell executes the previous reverse result.
Generally we need to calculate the reverse result according to the reverse derivative formula based on the forward output and the reverse result of the front layer, and return it. The reverse calculation of Dropout2d requires masking the reverse result of the front layer based on the mask matrix of the forward output, and then scaling according to keep_prob. The final implementation can get the correct calculation result.
Hook Function
Debugging deep learning networks is a big task for every practitioner in the field of deep learning. Since the deep learning network hides the input and output data as well as the inverse gradient of the intermediate layer operators, only the gradient of the network input data (feature quantity and weight) is provided, resulting in the inability to accurately sense the data changes of the intermediate layer operators, which reduces the debugging efficiency. In order to facilitate users to debug the deep learning network accurately and quickly, MindSpore designes Hook function in dynamic graph mode. Using Hook function can capture the input and output data of intermediate layer operators as well as the reverse gradient.
Currently, four forms of Hook functions are provided in dynamic graph mode: HookBackward operator and register_forward_pre_hook, register_forward_hook, register_backward_hook functions registered on Cell objects.
HookBackward Operator
HookBackward implements the Hook function in the form of an operator. The user initializes a HookBackward operator and places it at the location in the deep learning network where the gradient needs to be captured. In the forward execution of the network, the HookBackward operator outputs the input data as is without any modification. When the network back propagates the gradient, the Hook function registered on HookBackward will capture the gradient back propagated to this point. The user can customize the operation on the gradient in the Hook function, such as printing the gradient, or returning a new gradient.
The sample code is as follows:
import mindspore as ms
from mindspore import ops
ms.set_context(mode=ms.PYNATIVE_MODE)
def hook_fn(grad_out):
"""Print Gradient"""
print("hook_fn print grad_out:", grad_out)
hook = ops.HookBackward(hook_fn)
def hook_test(x, y):
z = x * y
z = hook(z)
z = z * y
return z
def net(x, y):
return ms.grad(hook_test, grad_position=(0, 1))(x, y)
output = net(ms.Tensor(1, ms.float32), ms.Tensor(2, ms.float32))
print("output:", output)
hook_fn print grad_out: (Tensor(shape=[], dtype=Float32, value= 2),)
output: (Tensor(shape=[], dtype=Float32, value= 4), Tensor(shape=[], dtype=Float32, value= 4))
For more descriptions of the HookBackward operator, refer to the API documentation.
register_forward_pre_hook Function in Cell Object
The user can use the register_forward_pre_hook function on the Cell object to register a custom Hook function to capture data that is passed to that Cell object. This function does not work in static graph mode and inside functions modified with @jit. The register_forward_pre_hook function takes the Hook function as an input and returns a handle object that corresponds to the Hook function. The user can remove the corresponding Hook function by calling the remove() function of the handle object. Each call to the register_forward_pre_hook function returns a different handle object. Hook functions should be defined in the following way.
def forward_pre_hook_fn(cell_id, inputs):
print("forward inputs: ", inputs)
Here cell_id is the name of the Cell object as well as the ID information, and inputs are the data passed forward to the Cell object. Therefore, the user can use the register_forward_pre_hook function to capture the positive input data of a particular Cell object in the network. The user can customize the operations on the input data in the Hook function, such as viewing, printing data, or returning new input data to the current Cell object. If the original input data of the Cell object is computed in the Hook function and then returned as new input data, these additional computation operations will act on the backpropagation of the gradient at the same time.
The sample code is as follows:
import numpy as np
import mindspore as ms
import mindspore.nn as nn
ms.set_context(mode=ms.PYNATIVE_MODE)
def forward_pre_hook_fn(cell_id, inputs):
print("forward inputs: ", inputs)
input_x = inputs[0]
return input_x
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.relu = nn.ReLU()
self.handle = self.relu.register_forward_pre_hook(forward_pre_hook_fn)
def construct(self, x, y):
x = x + y
x = self.relu(x)
return x
net = Net()
grad_net = ms.grad(net, grad_position=(0, 1))
x = ms.Tensor(np.ones([1]).astype(np.float32))
y = ms.Tensor(np.ones([1]).astype(np.float32))
output = net(x, y)
print(output)
gradient = grad_net(x, y)
print(gradient)
net.handle.remove()
gradient = grad_net(x, y)
print(gradient)
forward inputs: (Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]),)
[2.]
forward inputs: (Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]),)
(Tensor(shape=[1], dtype=Float32, value= [ 1.00000000e+00]), Tensor(shape=[1], dtype=Float32, value= [ 1.00000000e+00]))
(Tensor(shape=[1], dtype=Float32, value= [ 1.00000000e+00]), Tensor(shape=[1], dtype=Float32, value= [ 1.00000000e+00]))
If the user returns the newly created data directly in the Hook function, instead of returning the data obtained from the original input data after calculation, then the back propagation of the gradient will be cut off on that Cell object.
The sample code is as follows:
import numpy as np
import mindspore as ms
import mindspore.nn as nn
ms.set_context(mode=ms.PYNATIVE_MODE)
def forward_pre_hook_fn(cell_id, inputs):
print("forward inputs: ", inputs)
return ms.Tensor(np.ones([1]).astype(np.float32))
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.relu = nn.ReLU()
self.handle = self.relu.register_forward_pre_hook(forward_pre_hook_fn)
def construct(self, x, y):
x = x + y
x = self.relu(x)
return x
net = Net()
grad_net = ms.grad(net, grad_position=(0, 1))
x = ms.Tensor(np.ones([1]).astype(np.float32))
y = ms.Tensor(np.ones([1]).astype(np.float32))
gradient = grad_net(x, y)
print(gradient)
forward inputs: (Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]),)
(Tensor(shape=[1], dtype=Float32, value= [ 0.00000000e+00]), Tensor(shape=[1], dtype=Float32, value= [ 0.00000000e+00]))
To avoid running failure when scripts switch to graph mode, it is not recommended to call the register_forward_pre_hook function and the remove() function of the handle object in the construct function of the Cell object. In dynamic graph mode, if the register_forward_pre_hook function is called in the construct function of the Cell object, the Cell object will register a new Hook function every time it runs.
For more information about the register_forward_pre_hook function of the Cell object, refer to the API documentation.
register_forward_hook Function of Cell Object
The user can use the register_forward_hook function on the Cell object to register a custom Hook function that captures the data passed forward to the Cell object and the output data of the Cell object. This function does not work in static graph mode and inside functions modified with @jit. The register_forward_hook function takes the Hook function as an input and returns a handle object that corresponds to the Hook function. The user can remove the corresponding Hook function by calling the remove() function of the handle object. Each call to the register_forward_hook function returns a different handle object. Hook functions should be defined in the following way.
The sample code is as follows:
def forward_hook_fn(cell_id, inputs, outputs):
print("forward inputs: ", inputs)
print("forward outputs: ", outputs)
Here cell_id is the name of the Cell object and the ID information, inputs is the forward input data to the Cell object, and outputs is the forward output data of the Cell object. Therefore, the user can use the register_forward_hook function to capture the forward input data and output data of a particular Cell object in the network. Users can customize the operations on input and output data in the Hook function, such as viewing, printing data, or returning new output data. If the original output data of the Cell object is computed in the Hook function and then returned as new output data, these additional computation operations will act on the back propagation of the gradient at the same time.
The sample code is as follows:
import numpy as np
import mindspore as ms
import mindspore.nn as nn
ms.set_context(mode=ms.PYNATIVE_MODE)
def forward_hook_fn(cell_id, inputs, outputs):
print("forward inputs: ", inputs)
print("forward outputs: ", outputs)
outputs = outputs + outputs
return outputs
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.relu = nn.ReLU()
self.handle = self.relu.register_forward_hook(forward_hook_fn)
def construct(self, x, y):
x = x + y
x = self.relu(x)
return x
net = Net()
grad_net = ms.grad(net, grad_position=(0, 1))
x = ms.Tensor(np.ones([1]).astype(np.float32))
y = ms.Tensor(np.ones([1]).astype(np.float32))
gradient = grad_net(x, y)
print(gradient)
net.handle.remove()
gradient = grad_net(x, y)
print(gradient)
forward inputs: (Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]),)
forward outputs: [2.]
(Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]), Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]))
(Tensor(shape=[1], dtype=Float32, value= [ 1.00000000e+00]), Tensor(shape=[1], dtype=Float32, value= [ 1.00000000e+00]))
If the user returns the newly created data directly in the Hook function, instead of returning new output data that is obtained after the original output data is calculated, then the back propagation of the gradient will cut off on that Cell object, which can be seen in the use case illustration of the register_forward_pre_hook function.
To avoid running failure when the script switches to graph mode, it is not recommended to call the register_forward_hook function in the construct function of the Cell object and the remove() function of the handle object. In dynamic graph mode, if the register_forward_hook function is called in the construct function of the Cell object, the Cell object will register a new Hook function every time it runs.
For more information about the register_forward_hook function of the Cell object, please refer to the API documentation.
register_backward_hook Function of Cell Object
The user can use the register_backward_hook function on the Cell object to register a custom Hook function that captures the gradient associated with the Cell object when the network is back propagated. This function does not work in graph mode or inside functions modified with @jit. The register_backward_hook function takes the Hook function as an input and returns a handle object that corresponds to the Hook function. The user can remove the corresponding Hook function by calling the remove() function of the handle object. Each call to the register_backward_hook function will return a different handle object.
Unlike the custom Hook function used by the HookBackward operator, the inputs of the Hook function used by register_backward_hook contains cell_id, which represents the name and id information of the Cell object, the gradient passed to the Cell object in reverse, and the gradient of the reverse output of the Cell object.
The sample code is as follows:
def backward_hook_function(cell_id, grad_input, grad_output):
print(grad_input)
print(grad_output)
Here cell_id is the name and the ID information of the Cell object, grad_input is the gradient passed to the Cell object when the network is back-propagated, which corresponds to the reverse output gradient of the next operator in the forward process. grad_output is the gradient of the reverse output of the Cell object. Therefore, the user can use the register_backward_hook function to capture the backward input and backward output gradients of a particular Cell object in the network. The user can customize the operations on the gradient in the Hook function, such as viewing, printing the gradient, or returning the new output gradient. If you need to return the new output gradient in the Hook function, the return value must be in the form of tuple.
The sample code is as follows:
import numpy as np
import mindspore as ms
import mindspore.nn as nn
ms.set_context(mode=ms.PYNATIVE_MODE)
def backward_hook_function(cell_id, grad_input, grad_output):
print(grad_input)
print(grad_output)
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.conv = nn.Conv2d(1, 2, kernel_size=2, stride=1, padding=0, weight_init="ones", pad_mode="valid")
self.bn = nn.BatchNorm2d(2, momentum=0.99, eps=0.00001, gamma_init="ones")
self.handle = self.bn.register_backward_hook(backward_hook_function)
self.relu = nn.ReLU()
def construct(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
net = Net()
grad_net = ms.grad(net)
output = grad_net(ms.Tensor(np.ones([1, 1, 2, 2]).astype(np.float32)))
print(output)
net.handle.remove()
output = grad_net(ms.Tensor(np.ones([1, 1, 2, 2]).astype(np.float32)))
print("-------------\n", output)
(Tensor(shape=[1, 2, 1, 1], dtype=Float32, value=
[[[[ 1.00000000e+00]],
[[ 1.00000000e+00]]]]),)
(Tensor(shape=[1, 2, 1, 1], dtype=Float32, value=
[[[[ 9.99994993e-01]],
[[ 9.99994993e-01]]]]),)
[[[[1.99999 1.99999]
[1.99999 1.99999]]]]
-------------
[[[[1.99999 1.99999]
[1.99999 1.99999]]]]
When the register_backward_hook function and the register_forward_pre_hook function, and the register_forward_hook function act on the same Cell object at the same time, if the register_forward_pre_hook and the register_forward_hook functions add other operators for data processing, these new operators will participate in the forward calculation of the data before or after the execution of the Cell object, but the backward gradient of these new operators is not captured by the register_backward_hook function. The Hook function registered in register_backward_hook only captures the input and output gradients of the original Cell object.
The sample code is as follows:
import numpy as np
import mindspore as ms
import mindspore.nn as nn
ms.set_context(mode=ms.PYNATIVE_MODE)
def forward_pre_hook_fn(cell_id, inputs):
print("forward inputs: ", inputs)
input_x = inputs[0]
return input_x
def forward_hook_fn(cell_id, inputs, outputs):
print("forward inputs: ", inputs)
print("forward outputs: ", outputs)
outputs = outputs + outputs
return outputs
def backward_hook_fn(cell_id, grad_input, grad_output):
print("grad input: ", grad_input)
print("grad output: ", grad_output)
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.relu = nn.ReLU()
self.handle = self.relu.register_forward_pre_hook(forward_pre_hook_fn)
self.handle2 = self.relu.register_forward_hook(forward_hook_fn)
self.handle3 = self.relu.register_backward_hook(backward_hook_fn)
def construct(self, x, y):
x = x + y
x = self.relu(x)
return x
net = Net()
grad_net = ms.grad(net, grad_position=(0, 1))
gradient = grad_net(ms.Tensor(np.ones([1]).astype(np.float32)), ms.Tensor(np.ones([1]).astype(np.float32)))
print(gradient)
forward inputs: (Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]),)
forward inputs: (Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]),)
forward outputs: [2.]
grad input: (Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]),)
grad output: (Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]),)
(Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]), Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00]))
Here grad_input is the gradient passed to self.relu when the gradient is back-propagated, not the gradient of the new Add operator in the forward_hook_fn function. Here grad_output is the reverse output gradient of the self.relu when the gradient is back-propagated, not the reverse output gradient of the new Add operator in the forward_pre_hook_fn function. The register_forward_pre_hook and register_forward_hook functions work before and after the execution of the Cell object and do not affect the gradient capture range of the reverse Hook function on the Cell object.
To avoid running failure when the scripts switch to graph mode, it is not recommended to call the register_backward_hook function and the remove() function of the handle object in the construct function of the Cell object. In PyNative mode, if the register_backward_hook function is called in the construct function of the Cell object, the Cell object will register a new Hook function every time it runs.
For more information about the register_backward_hook function of the Cell object, please refer to the API documentation.