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import triton
from triton.testing import do_bench
import torch

x = torch.randn(1024*1024*16, device="cuda",  dtype=torch.bfloat16)

with torch.profiler.profile() as p:
    for _ in range(10):
        x.float().bfloat16()
        x + 1
p.export_chrome_trace("trace.json")

Biggest user is conversion ops that are not specialized (we specialize float -> bfloat16, but bfloat16 -> float still goes through generic path, and tbh I'm reluctant to specialize it). x+1 just for comparison of fully vectorized kernel perf

@pytorchbot cherry-pick --onto release/2.7 -c critical

Cherry picking #152396

The cherry pick PR is at #154541 and it is recommended to link a critical cherry pick PR with an issue. The following tracker issues are updated:

• [v2.7.1] Release Tracker #152627 (comment)