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Optimizer Design #4656
Optimizer Design #4656
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## Optimizer Design | ||
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### The Problem | ||
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A PaddlePaddle program, or a block, is a sequence of operators operating variables. A training program needs to do three kinds of works: | ||
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1. the forward pass, which computes intermediate results and the cost(s), | ||
1. the backward pass, which derives gradients from intermediate results and costs, and | ||
1. the optimization pass, which update model parameters to optimize the cost(s). | ||
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These works rely on three kinds of operators: | ||
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1. forward operators, | ||
1. gradient operators, and | ||
1. optimization operators. | ||
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It's true that users should be able to create all these operators manually by calling some low-level API, but it would be much more convenient if they could only describe the forward pass and let PaddlePaddle create the backward and optimization operators automatically. | ||
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In this design, we propose a high-level API that automatically derives the optimisation pass and operators from the forward pass. | ||
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### High-level Python API to describe the training process | ||
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1. User write code to describe the network: | ||
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```python | ||
images = layer.data("images") | ||
labels = layer.data("labels") | ||
w1 = pd.var("w1") | ||
b1 = pd.var("b1") | ||
hidden = layer.fc(images, w=w1, b=b1) | ||
cost = layer.mse(hidden, labels) | ||
``` | ||
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The above code snippet will create forward operators in [Block](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md). | ||
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2. Users create a certain kind of Optimizer with some argument. | ||
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```python | ||
optimizer = AdagradOptimizer(learing_rate=0.001) | ||
``` | ||
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3. Users use the optimizer to `minimize` a certain `cost` through updating parameters in parameter_list. | ||
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```python | ||
opt_op_list = optimizer.minimize(cost, parameter_list=[w1, b1]) | ||
``` | ||
The above code snippet will create gradient and optimization operators in Block. The return value of `minimize()` is list of optimization operators that will be run by session. | ||
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4. Users use Session/Executor to run this opt_op_list as target to do training. | ||
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```python | ||
sess.run(target= opt_op_list, ...) | ||
``` | ||
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#### Optimizer Python interface: | ||
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```python | ||
class Optimizer(object): | ||
def create_backward_pass(loss, parameter_list=None): | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Parameter and variable look like interchangeable in Python API. Not sure they are referred to the same concept. |
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""" | ||
Add gradient Operators into Block to Compute gradients of `loss` | ||
for parameters in parameter_list | ||
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Args: | ||
loss: an variable generated by cost function. | ||
parameter_list: parameters that need to compute gradient and update to minimize the lost | ||
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Returns: | ||
(parameters, gradients) pair list. | ||
""" | ||
return vars_grads | ||
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def create_optimization_pass(vars_grads): | ||
"""Add Operators to Apply gradients to variables. | ||
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Args: | ||
vars_grads: a list of (variable, gradient) pair to update. | ||
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Returns: | ||
optmization_op_list: a list of optimization operator that will optimize parameter with gradient. | ||
""" | ||
... | ||
return optmization_op_list | ||
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def minimize(loss, parameter_list): | ||
"""Add operations to minimize `loss` by updating `parameter_list `. | ||
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This method simply combines calls `create_backward_pass()` and | ||
`create_optimization_pass()`. | ||
""" | ||
vars_grads = create_backward_pass(loss) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. typo There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. done |
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update_ops = create_optimization_pass(var_grads) | ||
return update_ops | ||
``` | ||
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Users can inherit the Optimizer above to create their own Optimizer with some special logic, such as AdagradOptimizer. |
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the pseudo code here not format well.