Update how peak memory is measured (#150534)

Summary:
In the dashboard measurement script, AOTI needs to run Eager first to register the output pytree, so the peak memory compression ratio on the dashboard is always close to 1. Update AOTI run to use an extra warmup run, so the peak memory compression ratio measures the result at the run time instead of the compile time.

X-link: pytorch/pytorch#150534
Approved by: https://github.com/yushangdi

Reviewed By: clee2000

Differential Revision: D72395560

fbshipit-source-id: f37e493d851ea665f88972effbd225e8250a022f

Author: desertfire

userbenchmark/dynamo/dynamobench/common.py

@@ -3735,6 +3735,10 @@ def run(runner, args, original_dir=None):
             # AOTInductor doesn't support control flow yet
             runner.skip_models.update(runner.skip_models_due_to_control_flow)
             runner.skip_models.update(runner.skip_models_due_to_export_not_supported)
+
+            # For AOTI, we only measure the memory compression ratio at the run time
+            # instead of the compile time, so use a warmup run to trigger AOTI compilation.
+            args.use_warm_peak_memory = True
         elif args.backend == "torchao":
             assert "cuda" in args.devices, "Quantization requires CUDA device."
             assert args.bfloat16, "Quantization requires dtype bfloat16."