Re-land: Fix model initialization. (pytorch#6182)

benchmarks/torchbench_model.py

@@ -6,6 +6,7 @@
 import sys
 import torch
 import torch.nn as nn
+import torch.utils._pytree as pytree
 from torch._dynamo.testing import collect_results, reduce_to_scalar_loss
 from torch._dynamo.utils import clone_inputs
 import types
@@ -143,7 +144,6 @@ def is_compatible(self, dummy_benchmark_model, benchmark_experiment):
             break
         if matched:
           return False
-
     return True
 
 
@@ -193,12 +193,23 @@ def load_benchmark(self):
     # workaround "RuntimeError: not allowed to set torch.backends.cudnn flags"
     # torch.backends.__allow_nonbracketed_mutation_flag = True
 
-    if self.benchmark_experiment.accelerator == "cpu":
-      device = "cpu"
-    elif self.benchmark_experiment.accelerator == "cuda" and not self.benchmark_experiment.xla:
-      device = "cuda"
-    else:
-      device = str(self.benchmark_experiment.get_device())
+    benchmark = benchmark_cls(
+        test=self.benchmark_experiment.test,
+        device=self.benchmark_experiment.accelerator,
+        batch_size=self.benchmark_experiment.batch_size,
+    )
+
+    self.module, self.example_inputs = benchmark.get_module()
+
+    # Move the initialized model to XLA device.
+    if self.benchmark_experiment.xla:
+      device = self.benchmark_experiment.get_device()
+      self.module = self.module.to(device)
+      self.example_inputs = pytree.tree_map_only(torch.Tensor,
+                                                 lambda t: t.to(device),
+                                                 self.example_inputs)
+
+    self.benchmark_experiment.batch_size = benchmark.batch_size
 
     return benchmark_cls(
         test=self.benchmark_experiment.test,