[pipeline/rpc] implement a demo for PP with cuda rpc framework (#1470)

* support p2p communication with any type of object | pass test

* reconstruct pipeline schedule with p2p_v2.py(support communication with List[Any]) | pass test

* [engin/schedule] use p2p_v2 to recontruct pipeline_schedule

* [pipeline/rpc] implement a demo for PP with cuda rpc framework

* Delete p2p_v2.py

* Delete _pipeline_schedule_v2.py

* Delete test_object_list_p2p_v2.py

* Delete test_boardcast_send_recv_v2.py

* Delete test_cifar_with_data_pipeline_tensor_v2.py

tests/test_pipeline/test_cuda_rpc_pipeline.py

@@ -0,0 +1,102 @@
+import os
+import argparse
+
+import torch
+from torch import nn
+import torch.multiprocessing as mp
+import torch.distributed.rpc as rpc
+
+from colossalai.pipeline.rpc.PipelineBase import FillDrainPipelineEngine, OneFOneBPipelineEngine
+
+
+class TestModel(nn.Module):
+
+    def __init__(self, rank, world_size, feat_num, h) -> None:
+        super().__init__()
+        self.rank = rank
+        self.is_last_rank = rank == world_size - 1
+        self.linear_name = f'linear_{rank}'
+        if rank == 0:
+            setattr(self, self.linear_name, nn.Linear(feat_num, h))
+        elif rank == world_size - 1:
+            setattr(self, self.linear_name, nn.Linear(h, 1))
+        else:
+            setattr(self, self.linear_name, nn.Linear(h, h))
+
+    def forward(self, x) -> torch.Tensor:
+        linear: nn.Module = getattr(self, self.linear_name)
+        out: torch.Tensor = linear(x)
+
+        if self.is_last_rank:
+            out = out.sum()
+        return out
+
+
+def run_main(args):
+    torch.manual_seed(100)
+
+    sample_num = 128
+    feat_num = 10000
+    h = 10000
+    device = args.device
+    world_size = args.world_size
+    batch_size = 128
+    assert sample_num % batch_size == 0
+    batch_num = sample_num // batch_size
+    num_microbatches = world_size
+
+    input_sample = torch.randn((sample_num, feat_num), device=device)
+
+    module_partitions = [TestModel(rank, world_size, feat_num, h) for rank in range(world_size)]
+
+    engine = OneFOneBPipelineEngine(module_partitions=module_partitions,
+                                    chunk=1,
+                                    world_size=world_size,
+                                    num_microbatches=num_microbatches,
+                                    device=args.device,
+                                    max_outstanding=world_size,
+                                    use_interleave=False,
+                                    checkpoint=False)
+
+    for i in range(batch_num):
+        batch = input_sample[i * batch_size:(i + 1) * batch_size]
+        engine.forward_backward(batch)
+
+
+def run_worker(rank, args):
+    os.environ['MASTER_ADDR'] = args.master_addr
+    os.environ['MASTER_PORT'] = args.master_port
+
+    # config rpc
+    # if cuda is used, set_device_map is a must is configured
+    # for cuda is not supported in torch rpc by default
+    options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=args.num_worker_threads)
+
+    world_size = args.world_size
+    for rank_idx in range(world_size):
+        options.set_device_map(f'work{rank_idx}', {rank: rank_idx})
+
+    rpc.init_rpc(name=f'work{rank}', rank=rank, world_size=world_size, rpc_backend_options=options)
+
+    # in rpc mode, only rank 0 is needed to be coded
+    if rank == 0:
+        run_main(args)
+    # barrier here
+    rpc.shutdown()
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--world_size', type=int, default=2)
+    parser.add_argument('--device', type=str, default='cuda')
+    parser.add_argument('--master_addr', type=str, default='localhost')
+    parser.add_argument('--master_port', type=str, default='29020')
+    parser.add_argument('--num_worker_threads', type=str, default=128)
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    world_size = args.world_size
+    assert args.device in ['cpu', 'cuda'], "device must be cpu or cuda!"
+    mp.spawn(run_worker, args=(args,), nprocs=world_size)