mitkotak / fast_flops

FLOPS counter for all your GPU benchmarking needs

Fast FLOPS

... and where to find them 🐇

Ever needed to report FLOPs for your Torch/JAX code. We got you covered ! (More importantly, here's a survey on why bunnies flop)

Borrowed from https://gitlab.com/NERSC/roofline-on-nvidia-gpus/-/tree/roofline-hackathon-2020

Nsight Compute installation needed. Make sure the GPU counters are enabled

(Image Credit: Tuong Phung)

Workflow (Warning: Extremely clunky right now)

• Define your function that you wanna profile

  • JAX

    SIZE = 500
    x = jax.random.normal(jax.random.PRNGKey(0), (SIZE, SIZE))
    y = jax.random.normal(jax.random.PRNGKey(1), (SIZE, SIZE))
    
    def func(x, y):
        return jnp.einsum("ij,jk->ik", x, y)

  • Torch

    SIZE = 500
    x = torch.randn(SIZE, SIZE).to(device='cuda')
    y = torch.randn(SIZE, SIZE).to(device='cuda')
    
    def func(x, y):
        return torch.einsum("ij,jk->ik", x, y)

• Wrap the function in the flops_counter decorator

  from fast_flops import func_flops
  
  @flops_counter
  def func_flops(func, x, y):
      return func(x, y)

• Let it run through JIT (Don't worry we have warmups to cleanse the JIT overhead) and execute !

  • JAX

    func = jax.jit(func)
    func_flops(func, x, y)

  • Torch

    func = torch.compile(func, fullgraph=True, mode='max-autotune')
    func_flops(func, x, y)

• The pipeline can be executed using

  bash launch_profiler.sh examples/matmul/test_matmul_torch.py

  with the output looking something like

  Measured Time: 3.196823494576654e-05
  Measured GFLOP/s: 2353.1630879907675
  Measured FLOPS: 67108864.0

TODOs

• Only turn on ncu during the hot loop in addition to nvtx
• Axe run_profiler.py and postprocess.py. Ideally should work with python decorated_file.py
• Plotting utilieis and csv plumbing