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Tsanley: Understanding Tensor Programs

Project description

tsanley

experimental

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 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.

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.

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

> 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 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.
  3. Use the logs to annotate test.py.

Example

Let's revisit the earlier example, without our manual annotations. Suppose it resides in model.py.

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.

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:

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:

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.

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