benchmark_training.py: Add device arg

src/petals/cli/benchmark_training.py

@@ -19,6 +19,7 @@ petals.client.sequential_autograd.MAX_TOKENS_IN_BATCH = 1024
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--model", type=str, default="bigscience/bloom-petals")
+    parser.add_argument("--device", type=str, default="cpu")
     parser.add_argument("--task", type=str, default="cls")
     parser.add_argument("-i", "--initial_peers", type=str, nargs='+',
         default=["/dns/bench.petals.ml/tcp/31337/p2p/QmehSoMKScoMF3HczLwaLVnw2Lgsap4bhAMrULEzGc1fSV"])
@@ -53,6 +54,7 @@ def benchmark_training(process_idx, args):
         model = DistributedBloomForCausalLM.from_pretrained(
             args.model, initial_peers=args.initial_peers, tuning_mode="deep_ptune",
             pre_seq_len=args.pre_seq_len)
+    model = model.to(args.device)
     opt = torch.optim.Adam(model.parameters())
     logger.info(f"Created model: {process_idx=} {model.device=}")
 
@@ -60,9 +62,9 @@ def benchmark_training(process_idx, args):
     fwd_times = []
     bwd_times = []
     for step in range(args.n_steps):
-        input_ids = torch.randint(100, 10000, size=(args.batch_size, args.seq_len))
+        input_ids = torch.randint(100, 10000, size=(args.batch_size, args.seq_len), device=args.device)
         if args.task == "cls":
-            labels = torch.randint(0, 2, size=[args.batch_size])
+            labels = torch.randint(0, 2, size=[args.batch_size], device=args.device)
         else:
             labels = input_ids