Metadata-Version: 2.1
Name: tsanley
Version: 0.2.0
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)
        
        Tsanley is a shape analyzer for tensor programs, using popular tensor libraries: `tensorflow`, `pytorch`, `numpy`. Plugs into your existing code seamlessly, with minimal changes.
        
        Builds upon the 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 against 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` analyzer for shape checking in `foo`, we add a function `setup_named_dims` *before* calling `test_foo`, label tensor variables by their expected shorthand shapes (e.g., `b,d`) and then execute the code normally.
        
        
        ```python
        def foo(x):
            x: 'b,t,d' #shape check: ok!               [line 36]
            y: 'b,d' = x.mean(dim=0)  # error!         [line 37]
            z: 'b,d' = x.mean(dim=1) #shape check: ok! [line 38]
        
        def test_foo():
            import torch
            x = torch.Tensor(10, 100, 1024)
            foo(x)
        
        def setup_named_dims():
            from tsalib import dim_vars
            #declare the named dimension variables using the tsalib api
            #e.g., 'b' stands for 'Batch' dimension with size 10
            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
        
        
        if __name__ == '__main__': 
            setup_named_dims()
            test_foo()
        ```
        
        On executing the above program, `tsanley` tracks shapes of tensor variables (`x`, `y`, `z`) in function `foo` and reports following shape check results.
        
        #### 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: (b:10, 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 ..
        ```
        
        #### What does setup_named_dims do?
        
        - Declare the named dimension variables (using `dim_vars`) -- using them we can specify the expected shape of tensor variables in the code. For example, here we declare 3 dimension variables, `Batch`, `Length` and `Hidden`, and refer to them via shorthand names `b`,`t`, `d`. 
        - We use shorthand names to label tensor 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). We can specify names with wildcards, e.g., `Resnet.*` to track all functions in the `Resnet` class.
        
        See examples in [models](models/) directory.
        
        ### Installation
        
        ```
        pip install tsanley
        ```
        
        ### Annotation
        
        `tsanley` can also annotate tensor variables in existing *executable* code with shape labels. This is useful when trying to understand external open-source code or labeling one's own code.
        
        Suppose, we have some un-annotated code residing in file `model.py`.
        
        1. First, generate *shape logs* by adding `setup_named_dims` to the `model.py`.
        2. Execute `model.py`. The logs are stored in `/tmp/shape_log.json`.
        2. Use the logs to annotate `test.py`.
        
        #### Example
        Let's revisit the earlier example, without our manual annotations. Suppose it resides in `model.py`.
        
        ```python
        def foo(x):
            y = x.mean(dim=0) 
            z = x.mean(dim=1) 
        
        def test_foo():
            import torch
            x = torch.Tensor(10, 100, 1024)
            foo(x)
        ```
        
        We add `setup_named_dims` to the code, and execute it.
        
        ```python
        def setup_named_dims():
            from tsalib import dim_vars
            #declare the named dimension variables using the tsalib api
            #e.g., 'b' stands for 'Batch' dimension with size 10
            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=False) # debug=False
        
        if __name__ == '__main__': 
            setup_named_dims()
            test_foo()
        ```
        
        This generates the shape logs in `/tmp/shape_log.json`. Flag `check_tsa=False` ensures no shape checks are performed by `tsanley`.
        
        Now, annotate `foo` with the command:
        
        > tsa annotate -f model.py
        
        The output is a file `tsa_model.py` with `foo` updated as follows:
        
        ```python
        def foo(x):
            y: 't,d' = x.mean(dim=0) 
            z: 'b,d' = x.mean(dim=1) 
        ```
        
        `tsanley` makes smart guesses to map runtime shape values (`100`) to the shorthand names (`t`). If we do not declare the dimension names using `dim_vars` in `setup_named_dims`, we get the following annotation:
        
        ```python
        def foo(x):
            y: '100,1024' = x.mean(dim=0) 
            z: '10,1024' = x.mean(dim=1) 
        ```
        
        
        ### Status: Experimental
        
        `tsanley` performs a best-effort shape tracking during program execution. 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), but remain to be tested.
        
        
        
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
