Metadata-Version: 2.1
Name: tsanley
Version: 0.1.1
Summary: Tsanley: Understanding Tensor Programs
Home-page: https://github.com/ofnote/tsanley
Author: Nishant Sinha
Author-email: nishant@offnote.co
License: Apache 2.0
Description: # tsanley
        ![experimental](https://img.shields.io/badge/stability-experimental-orange.svg)
        
        A shape analyzer for tensor programs, using popular tensor libraries: `tensorflow`, `pytorch`, `numpy`, ...
        
        Builds upon a library [tsalib](https://github.com/ofnote/tsalib) for specifying, annotating and transforming tensor shapes using **named dimensions**.
        
        ### Quick Start
        
        `tsanley` discovers shape errors at runtime by checking the runtime tensor shapes with the user-specified shape annotations. Tensor shape annotations are specified in the `tsalib` shape shorthand notation, e.g., `x: 'btd'`.
        
        More details on the shorthand format [here](https://github.com/ofnote/tsalib/blob/master/notebooks/shorthand.md).
        
        #### Example
        
        Suppose we have the following functions `foo` and `test_foo` in our existing code. To setup `tsanley`, we add the following code *before* `test_foo` is called (in the `test` function):
        
        - Declare the named dimension variables (using `dim_vars`) -- these help specify the expected shape of tensor variables used in the code. For example, here we declare 3 dimension variables, referred to via shorthand names `b`, `t`, `d`. We use these shorthand names to label variables and check their shapes in one or more functions, e.g., `foo` here.
        - Initialize the `tsanley` analyzer by calling `init_analyzer`: parameter `trace_func_names` takes a list of function names as Unix shell-style wildcards (using the `fnmatch` library).
        
        ```python
        def foo(x):
            x: 'b,t,d' #shape check: ok!              [line 36]
            y: 'b,d' = x.mean(dim=0) #error: dim should be 1  [line 37]
            #   ^ 
            #   | tsanley detects shape violation
        
            z: 'b,d' = x.mean(dim=1) #shape check: ok! [line 38]
        
        def test_foo():
            import torch
            from tsalib import get_dim_vars
        
            # get the declared dimension sizes: 10, 100, 1024
            B, L, D = get_dim_vars('b t d') 
            x = torch.Tensor(B, L, D)
            foo(x)
        
        def test():
            #declare the named dimension variables using the tsalib api
            from tsalib import dim_vars
            dim_vars('Batch(b):10 Length(t):100 Hidden(d):1024')
        
            # initialize tsanley's dynamic shape analyzer
            from tsanley.dynamic import init_analyzer
            init_analyzer(trace_func_names=['foo'], show_updates=True) #check_tsa=True, debug=False
        
            test_foo()
        
        if __name__ == '__main__': test()
        ```
        
        On executing the above program, `tsanley` tracks shapes of tensor variables (`x`, `y`, `z`) in function `foo` and reports shape check successes and failures.
        
        #### Output
        
        ```bash
        > Analyzing function foo 
          
        Update at line 36: actual shape of x = b,t,d 
          >> shape check succeeded at line 36 
          
        Update at line 37: actual shape of y = t,d 
          >> FAILED shape check at line 37 
          expected: (d:1024,), actual: (100, 1024) 
          
        Update at line 38: actual shape of z = b,d 
          >> shape check succeeded at line 38 
        saving shapes to /tmp/shape_log.json ..
        ```
        
        
        See examples in [models](models/) directory.
        
        ### Installation
        
        ```
        pip install tsanley
        ```
        
        ### Status: Experimental
        
        `tsanley` performs a best-effort shape tracking when the program runs. Here are a few tricky scenarios:
        
        - calling same function multiple times -- shape values from only the last call are cached.
        - recursive calls -- not handled.
        
        Tested with `pytorch` examples. `tensorflow` and `numpy` programs should also work (`tsalib` supported backends).
Platform: POSIX
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
