[p2p]add object list send/recv (#1024)

* [CLI] add CLI launcher

* Revert "[CLI] add CLI launcher"

This reverts commit df7e6506d4500af6a9220ef7fe4d3c7b1daebd4c.

* [p2p]add object list send recv

* refactor for code reusability

* polish

tests/test_comm/test_object_list_p2p.py

+from functools import partial
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from colossalai.communication.p2p import send_forward, recv_forward, send_backward, recv_backward, send_forward_recv_backward, send_backward_recv_forward
+from colossalai.context import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.initialize import launch
+from colossalai.utils import free_port, get_current_device
+from colossalai.testing import rerun_if_address_is_in_use
+
+CONFIG = dict(parallel=dict(pipeline=2))
+torch.manual_seed(123)
+LIST_LENGTH = 3
+TENSOR_SIZE = torch.Size((3, 3))
+TENSOR_SIZE_LIST = [TENSOR_SIZE for i in range(LIST_LENGTH)]
+data = torch.rand(3, 3)
+data_list = [torch.rand(3, 3) for i in range(LIST_LENGTH)]
+grad = torch.rand(3, 3)
+grad_list = [torch.rand(3, 3) for i in range(LIST_LENGTH)]
+
+
+def check_send_recv_forward():
+    if gpc.get_local_rank(ParallelMode.PIPELINE) == 0:
+        device = torch.device('cuda:0')
+        data_to_send = data.to(device)
+        data_list_to_send = []
+        for data_in_list in data_list:
+            data_list_to_send.append(data_in_list.to(device))
+        send_forward(data_to_send)
+        send_forward(data_list_to_send)
+    else:
+        device = torch.device('cuda:1')
+        data_recv = recv_forward(TENSOR_SIZE)
+        data_list_recv = recv_forward(TENSOR_SIZE_LIST)
+        data_to_check = data.to(device)
+        assert data_recv.equal(data_to_check)
+        for data_recv, data_send in zip(data_list_recv, data_list):
+            data_to_check = data_send.to(device)
+            assert data_recv.equal(data_to_check)
+
+
+def check_send_recv_backward():
+    if gpc.get_local_rank(ParallelMode.PIPELINE) == 0:
+        device = torch.device('cuda:0')
+        grad_recv = recv_backward(TENSOR_SIZE)
+        grad_list_recv = recv_backward(TENSOR_SIZE_LIST)
+        grad_to_check = grad.to(device)
+        assert grad_recv.equal(grad_to_check)
+        for grad_recv, grad_send in zip(grad_list_recv, grad_list):
+            grad_to_check = grad_send.to(device)
+            assert grad_recv.equal(grad_to_check)
+    else:
+        device = torch.device('cuda:1')
+        grad_to_send = grad.to(device)
+        grad_list_to_send = []
+        for grad_in_list in grad_list:
+            grad_list_to_send.append(grad_in_list.to(device))
+        send_backward(grad_to_send)
+        send_backward(grad_list_to_send)
+
+
+def check_send_recv_forward_backward():
+    if gpc.get_local_rank(ParallelMode.PIPELINE) == 0:
+        device = torch.device('cuda:0')
+        data_list_to_send = []
+        for data_in_list in data_list:
+            data_list_to_send.append(data_in_list.to(device))
+        grad_list_recv = send_forward_recv_backward(data_list_to_send, TENSOR_SIZE_LIST)
+
+        for grad_recv, grad_send in zip(grad_list_recv, grad_list):
+            grad_to_check = grad_send.to(device)
+            assert grad_recv.equal(grad_to_check)
+    else:
+        device = torch.device('cuda:1')
+        grad_list_to_send = []
+        for grad_in_list in grad_list:
+            grad_list_to_send.append(grad_in_list.to(device))
+        data_list_recv = send_backward_recv_forward(grad_list_to_send, TENSOR_SIZE_LIST)
+        for data_recv, data_send in zip(data_list_recv, data_list):
+            data_to_check = data_send.to(device)
+            assert data_recv.equal(data_to_check)
+
+
+def check_layer(rank, world_size, port):
+    launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    check_send_recv_forward()
+    check_send_recv_backward()
+    check_send_recv_forward_backward()
+    gpc.destroy()
+    torch.cuda.empty_cache()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_object_list_p2p():
+    world_size = 2
+    run_func = partial(check_layer, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_object_list_p2p()