always set deterministic for xpu accuracy test (#149028)

Summary:
On Intel Max 1550, models like Super_SloMo can actually pass accuracy test after set deterministic, because we do not use atomic in upsampling bilinear backward in some cases when running on XPU. Furthermore, I guess the only reason not to set deterministic on these models is just avoiding errors. We should use warn_only = True.

X-link: pytorch/pytorch#149028
Approved by: https://github.com/guangyey, https://github.com/desertfire

Reviewed By: clee2000

Differential Revision: D72218565

fbshipit-source-id: ecf3fc337c48dc9e16d124ffc50ab444c1af763a

Co-authored-by: Yu, Guangye <106960996+guangyey@users.noreply.github.com>

Author: 2 people

userbenchmark/dynamo/dynamobench/common.py

@@ -3542,6 +3542,8 @@ def run(runner, args, original_dir=None):
         }:
             # some of the models do not support use_deterministic_algorithms
             torch.use_deterministic_algorithms(True)
+        if args.devices == ["xpu"]:
+            torch.use_deterministic_algorithms(True, warn_only=True)
         os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
         # TODO(eqy): revisit when cuBLASLt workspace size is bumped
         # if args.only is not None and args.only in {