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https://github.com/hrbigelow/pyctb

Python Customized Tracebacks
https://github.com/hrbigelow/pyctb

debugging-tool pytorch tensorflow

Last synced: 12 days ago
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Python Customized Tracebacks

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README 

        
# Python Customized Tracebacks



## Installation



```

pip install pyctb 

```



## Synopsis



```python

import pyctb



# add a pre-existing group of custom rendering functions

pyctb.add_group('torch')



# or add your own:

# pyctb.add(your_cls, your_render_func)

# pyctb.add(your_cls2, your_render_func2)

# ...



# for each frame in the traceback:

# display 1 piece of code context before the current executing piece

# display 0 pieces of code context after the current executing piece

pyctb.config(before_ctx=1, after_ctx=0)



# activate the custom tracebacks

pyctb.on()



# your program code below.  any exception will generate a customized traceback

...



# exceptions will now generate the default traceback as before

pyctb.off()

```



## Introduction



pyctb is tool for customizing the information displayed in a Python traceback.

It is useful to quickly see the values of arguments or operands used at the

point where your program throws an exception, without the need for a debugger.

For each traceback frame, pyctb adds extra lines of the form `var =

render_func(value)` for each variable in the currently executing function of

that frame, using a `render_func` registered with that class.  By default,

`object` is registered to use `repr`.  The user may register custom functions for

rendering specific types of their choice.  Such specific rendering functions

are needed for types that have too large of an output with just `repr`.



pyctb relies on the excellent

[stack_data](https://github.com/alexmojaki/stack_data) repo from Alex Hall for

inspecting the frame information variable names and values.



You may register a new rendering function using:



`pyctb.add(cls, func)`



Objects are rendered using the most specific (in method-resolution order)

rendering function registered.  For example:



```python

pyctb.add(Animal, render_animal)

pyctb.add(Cat, render_cat)

pyctb.add(Tabby, render_tabby)

```



Then, a `Tabby` will be rendered with `render_tabby`.  A `Persian` will use

`render_cat`, and `Dog` will use `render_animal`, and so forth.  By default,

`object` is registered with `repr`.



## Tests



```bash

# compare default and customized tracebacks using the `torch` group of

# renderers.  In each frame of the traceback,  and 

# specify the number of pieces of code context to be displayed before and after

# each currently executing piece.  Using 0 for both  and

#  is equivalent to the default behavior.

python -m tests.torch_test  



# same but with tensorflow tests.

python -m tests.tf_test  

```



The `tests.torch_test` runs two functions called `binop_test` and

`matmul_test`.  Each function raises an exception, and it is run twice.  First

with `pyctb` turned off, and then with it turned on.  The default and custom

tracebacks are shown as output.



Notice that the custom traceback provides additional `name = value` lines for

all arguments to the function or operand that raised the exception.  In this

case, using the `torch` group, the operands were `torch.Tensor`, and the custom

rendering function displays them in a format `shape:dtype:device`.



Additionally, you can see that there is one piece of leading code context (if

it exists in that frame) shown before the currently executing piece.



```

# Run the torch_tests using 1 piece of leading context and non trailing context

(pytorch181) henry@henry-gs65:pyctb$ python -m tests.torch_test 1 0

========= Default traceback for binop_test =========

Traceback (most recent call last):

  File "/home/henry/ai/projects/pyctb/tests/torch_test.py", line 25, in main

    test()

  File "/home/henry/ai/projects/pyctb/tests/torch_test.py", line 16, in binop_test

    return a / b

RuntimeError: The size of tensor a (5) must match the size of tensor b (6) at non-singleton dimension 1



========= Custom traceback for binop_test =========

1 pieces of leading context

0 pieces of trailing context

Custom Traceback (most recent call last):

  File "/home/henry/ai/projects/pyctb/tests/torch_test.py", line 34, in main

    try:

-->     test()

         test = 

  File "/home/henry/ai/projects/pyctb/tests/torch_test.py", line 16, in binop_test

    b = torch.randn(3,6,7)

--> return a / b

         a = [3,5,7]:float32:cpu

         b = [3,6,7]:float32:cpu

RuntimeError: The size of tensor a (5) must match the size of tensor b (6) at non-singleton dimension 1



========= Default traceback for matmul_test =========

Traceback (most recent call last):

  File "/home/henry/ai/projects/pyctb/tests/torch_test.py", line 25, in main

    test()

  File "/home/henry/ai/projects/pyctb/tests/torch_test.py", line 9, in matmul_test

    d = torch.matmul(a, b)

RuntimeError: Expected batch2_sizes[0] == bs && batch2_sizes[1] == contraction_size to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)



========= Custom traceback for matmul_test =========

1 pieces of leading context

0 pieces of trailing context

Custom Traceback (most recent call last):

  File "/home/henry/ai/projects/pyctb/tests/torch_test.py", line 34, in main

    try:

-->     test()

         test = 

  File "/home/henry/ai/projects/pyctb/tests/torch_test.py", line 9, in matmul_test

    c = b * 5

--> d = torch.matmul(a, b)

         a = [3,5,7]:float32:cpu

         b = [3,5,7]:float32:cpu

RuntimeError: Expected batch2_sizes[0] == bs && batch2_sizes[1] == contraction_size to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)

```