Specify Trainer(benchmark=False) in parity benchmarks (#13182)

Co-authored-by: Carlos Mocholí <[email protected]>

Author: 2 people

tests/benchmarks/test_basic_parity.py

@@ -149,7 +149,6 @@ def vanilla_loop(cls_model, idx, device_type: str = "cuda", num_epochs=10):
 
 def lightning_loop(cls_model, idx, device_type: str = "cuda", num_epochs=10):
     seed_everything(idx)
-    torch.backends.cudnn.deterministic = True
 
     model = cls_model()
     # init model parts
@@ -162,6 +161,7 @@ def lightning_loop(cls_model, idx, device_type: str = "cuda", num_epochs=10):
         gpus=1 if device_type == "cuda" else 0,
         logger=False,
         replace_sampler_ddp=False,
+        benchmark=False,
     )
     trainer.fit(model)
 

tests/benchmarks/test_sharded_parity.py

@@ -137,15 +137,31 @@ def plugin_parity_test(
     ddp_model = model_cls()
     use_cuda = gpus > 0
 
-    trainer = Trainer(fast_dev_run=True, max_epochs=1, gpus=gpus, precision=precision, strategy="ddp_spawn")
+    trainer = Trainer(
+        fast_dev_run=True,
+        max_epochs=1,
+        accelerator="gpu",
+        devices=gpus,
+        precision=precision,
+        strategy="ddp_spawn",
+        benchmark=False,
+    )
 
     max_memory_ddp, ddp_time = record_ddp_fit_model_stats(trainer=trainer, model=ddp_model, use_cuda=use_cuda)
 
     # Reset and train Custom DDP
     seed_everything(seed)
     custom_plugin_model = model_cls()
 
-    trainer = Trainer(fast_dev_run=True, max_epochs=1, gpus=gpus, precision=precision, strategy="ddp_sharded_spawn")
+    trainer = Trainer(
+        fast_dev_run=True,
+        max_epochs=1,
+        accelerator="gpu",
+        devices=gpus,
+        precision=precision,
+        strategy="ddp_sharded_spawn",
+        benchmark=False,
+    )
     assert isinstance(trainer.strategy, DDPSpawnShardedStrategy)
 
     max_memory_custom, custom_model_time = record_ddp_fit_model_stats(