PaddlePaddle Fluid Source Code Overview¶

Examples: https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/v2/fluid/tests/book

Core: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework

Operator: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators

Optimizer: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/optimizer

Memory: https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/memory

Compile Time¶

The following defines the NN. The definition goes into this protocol buffer.

x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')

y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(x=cost)

sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)

Variables: x, y, y_predict, cost and avg_cost. Python
Layers: fluid.layers.data, fluid.layers.fc and fluid.layers.mean are layers. Python
- Every Layer has one or more operators and variables/parameters
  - All the operators are defined at paddle/operators/. Other worth-looking files:
    - Base class: paddle/framework/operator.h
    - Operator Registration: paddle/framework/op_registry.h
    - Operator Lookup: paddle/framework/op_info.h
Optimizer: fluid.optimizer.SGD. It does the following
- Add backward operators. [Python, C++]
- Add optimizer operators. [Python, C++]

Run Time¶

The following evaluates the NN. Instantiates all the variables, operators.

place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)

# Allocate memory. Initialize Parameter.
exe.run(fluid.default_startup_program())

# Allocate memory. Do computation.
exe.run(fluid.default_main_program(),
        feed=feeder.feed(data),
        fetch_list=[avg_cost])

Place: place. one of CPU, GPU or FPGA. C++
- The device handle are at paddle/platform/device_context.h
Executor: fluid.Executor(place). [Python, C++]
- Feeds the data: feed=feeder.feed(data)
- Evaluates all the operators
- Fetches the result: fetch_list=[avg_cost]
Other worth looking files:
- Scope: paddle/framework/scope.h. Where all the variables live
  - Variable: paddle/framework/variable.h. Where all the data (most likely tensors) live
    - Tensor: paddle/framework/tensor.h. Where we allocate memory through paddle/memory/