torch_guards/guard_drop.py at main · jeromean/torch_guards · GitHub

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import torch
import logging
torch._logging.set_logs(recompiles=True, guards=True)

def simple_model(x):
    return x * 2

def reset_compile():
    torch._dynamo.reset()
    torch.compiler.reset()
    torch._dynamo.utils.counters.clear()

def print_stats(msg):
    print("="*80)
    print(f"*************** {msg} ****************")
    print("="*80)
    stats = torch._dynamo.utils.compile_times()
    print(stats)
    print("-"*40)
    print(torch._dynamo.utils.counters)
    print("-"*40)
    print("="*80)

def run_without_optimization(in1, in2):
    compiled_model =  torch.compile(simple_model)
    compiled_model(in1)
    # the second compile is for another shape and its expected to recompile the graph which can be confirmed from the logs
    # [__recompiles] Recompiling function simple_model in /home/jeromean/experiment/experiments/pytorch_guards/guard_drop.py:5
    # [__recompiles]     triggered by the following guard failure(s):
    # [__recompiles]     - 0/0: tensor 'x' size mismatch at index 0. expected 4, actual 5
    compiled_model(in2)
    print_stats("Base Stats")


def run_with_optimization(in1, in2):
    #dynanic=True aids to make use of SymInts and relax guards
    compiled_model =  torch.compile(simple_model, dynamic=True)
    compiled_model(in1)
    # the second compile should not result in a recompilation
    compiled_model(in2)

    print_stats("Dynamic=True")

if __name__ == "__main__":

    input1 = torch.randn(4)
    print("input 1 : ", input1)
    input2 = torch.randn(5)
    print("input 2 : ", input2)

    run_without_optimization(input1, input2)

    reset_compile()

    run_with_optimization(input1, input2)