[Intel gpu] always set deterministic for xpu accuracy test (pytorch#149028)

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.

Pull Request resolved: pytorch#149028
Approved by: https://github.com/guangyey, https://github.com/desertfire

Co-authored-by: Yu, Guangye <[email protected]>

Author: 2 people

benchmarks/dynamo/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 {