Supported Grammars¶

Control flow keywords¶

Control flow means those keywords that controls the execution order of program statements, for example if-elif-else, while . Conditional operation and loop were implemented as cond, while_loop APIs in PaddlePaddle static graph. If the condition of a Python dygraph control flow depends on PaddlePaddle Tensor, the ProgramTranslator will convert the control flow into equivalent PaddlePaddle control flow APIs, else it will still be executed as Python control flow. The transformations of those control flow keywords are listed below:

1. if-elif-else statements

If the condition of if <condition> is Tensor, ProgramTranslator will turn this if-elif-else statement to equivalent PaddlePaddle static graph cond statements, otherwise the if-elif-else statement is executed as normal Python conditional statement. Note that cond API only accepts input conditional Tensor with numel equals to 1, so please use this kind of Tensor to write dygraph conditional statement, other Tensors will cause error.

2. while loop

If the condition of while is Tensor, ProgramTranslator will turn this while statement to equivalent PaddlePaddle static graph while_loop statements, otherwise the while statement is executed as normal Python while loop statement. Note that while_loop API only accepts input conditional Tensor with numel equals to 1, so please use this kind of Tensor to write dygraph loop condition statement, other Tensors will cause error.

3. for loop

3.1 for _ in range(__) loop

Firstly, ProgramTranslator will transform it into equivalent Python while loop, then convert dygraph to static graph by same logic of while loop.

3.2 for _ in x loop

If x is a Python container, iterator, or generator, it will be executed as original Python statement. Otherwise x is a Tensor, ProgramTranslator will transform the loop into PaddlePaddle static graph loop and fetches x[0], x[1], ... as loop iteration variable in each loop iteration.

3.3 for idx, val in enumerate(x) loop

If x is a Python container, iterator, or generator, it will be executed as original Python statement. Otherwise x is a Tensor, Program Translator will transform the loop into PaddlePaddle static graph loop. The idx will be transformed to 1-D tensor with value 0, 1, ... and the val will be transformed to x[0], x[1], ... in each loop iteration.

4. break, continue

ProgramTranslator supports break, continue statements in loop. ProgramTranslator will add some PaddlePaddle static graph cond statements to skip execution of corresponding part when break, continue condition is meet.

5. return

ProgramTranslator supports return in a conditonal block or loop body, not necessary to be at the end of a function. It also supports returning tuple with various length of Tensors with different dtype. The implementation is adding some PaddlePaddle static graph cond statement to skipparts of code when return is triggered.

Some Python basic operators¶

1. +, -, *, /, **, >, <, >= , <=, == etc.

Because PaddlePaddle static graph overrides those Python basic arithmetic operators and comparison operators, ProgramTranslator can support those operators.

2. and, or, not logical operators

Python has and, or, not keywards as basic logical operators, ProgramTranslator will check whether the variables of the logical operators are Tensors, if they are Tensors, ProgramTranslator replaces the and, or, not statements into corresponding PaddlePaddle static graph logical operator and run it.

3. Type casting

In dygraph mode, users can use Python type casting grammar. For instance, if x is a Tensor, float(x) casts the data type of x to float. ProgramTranslator will check whether x is a Tensor during run time, if it is, the casting sentence will be modified to PaddlePaddle static graph cast API so that its dtype can be changed in the dygraph to static transformation.

Python functions¶

1. print

In dygraph mode, print(x) will print Tensor value if x is a Tensor. ProgramTranslator converts the built-in print to PaddlePaddle static graph Print API during dygraph to static graph transformation if the arguments are Tensors, otherwise ProgramTranslator won’t convert the print.

2. len

If x is a Tensor, len(x) can get the length at 0-dimension of x in dygraph mode. ProgramTranslator turns it to PaddlePaddle static graph shape API and returns the 0-dimension of the shape, else if x is a TensorArray, then len(x) will be transformed to static graph API control_flow.array_length to return the length of TensorArray. In other cases, the len function will be executed as Python built-in len

3. lambda expression

ProgramTranslator supports Python lambda expression and it modifies code to return the expected result.

4. Calling function

If the transformed function calls another function, ProgramTranslator also transform the called function. The benefit is that users can add one decorator at the outside function to do transformation, no need to add the decorator for each function. Note that ProgramTranslator doesn’t support that a function calls itself recursively, the details is in the unsupported grammars section below.

Errors and Exceptions¶

1. assert

If x is a Tensor, assert x statement can assert x to be True or non-zero value in dygraph mode. ProgramTranslator converts the statement into PaddlePaddle static graph Assert API to support this grammar.

Python containers¶

1. list: if all elements in a list are Tensors, then ProgramTranslator converts it to TensorArray. PaddlePaddle static graph TensorArray supports append, pop, and modify, other list operations such as sort cannot be supported. When not all elements in a list are Tensors, ProgramTranslator will treat it as normal Python list.

2. dict: ProgramTranslator will add the Tensors in a dict into PaddlePaddle static graph Program, so dict is supported by ProgramTranslator.

Unsupported grammars¶

1. Use the shape of a tensor whose shape has been changed.

For example, x = reshape(x, shape=shape_tensor) , then use x.shape[0] to do other operation. Due to the difference between dygraph and static graph, it is okay in dygraph but it will fail in static graph. The reason is that APIs return computation result in dygraph mode, so x.shape has deterministic value after calling reshape . However, static graph doesn’t have the value shape_tensor during building network, so PaddlePaddle doesn’t know the value of x.shape after calling reshape. PaddlePaddle static graph will set -1 to represent unknown shape value for each dimension of x.shape in this case, not the expected value. Similarily, calling the shape of the output tensor of those APIs which change the shape, such as expend, cannot be converted into static graph properly.

We suggest to set fixed shape value as much as possible, reduce the operations that change tensor shape.

2. List of list of Tensor

For example: l = [[tensor1, tensor2], [tensor3, tensor4]], because ProgramTranslator transformed a list whose elements are all Tensors into PaddlePaddle static graph TensorArray, but TensorArray doesn’t support multi-dimensions, ProgramTranslator cannot run this case.

We suggest to use 1-D list at most time, or use PaddlePaddle API create_array, array_read, array_write to control TensorArray.

3. Convert Tensor to numpy array and do operation

For example, user doesn’t return Tensor in the decorated function but call numpy.array(tensor) to convert Tensor to numpy array and then use numpy API to compute on it. In dygraph mode, it is okey because Tensor has value, but Tensor is variable for building network in static graph mode, it doesn’t contain value if not in static graph running time, so we cannot do numpy calculation on it.

We suggest to use PaddlePaddle APIs to replace numpy API in this case.

4. A function calls itself recursively

ProgramTranslator doesn’t support a function calls itself recursively, the reason is that recursive function usually uses if-else for a condition to stop the recursion, the stop condition will be transformed to a cond in static graph mode. Since cond just builds network, it cannot determine how many times it recursively builds network during network built stage, so the function will recursively call itself and build network until stack overflow. Due to above reason, ProgramTranslator cannot support a function calls itself recursively now.

We suggest to write non-recursive function in this case.