[pipeline/rpc] implement distributed optimizer | test with assert_close (#1486)

* 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

* [pipeline/rpc] support interleaving | fix checkpoint bug | change logic when dispatch data in work_list to ensure steady 1F1B

* [pipeline/rpc] implement distributed optimizer | test with assert_close

* [pipeline/rpc] implement distributed optimizer | test with assert_close

tests/test_pipeline/rpc_test_utils.py

@@ -0,0 +1,84 @@
+import os
+import argparse
+
+import torch
+from torch import nn
+import torch.multiprocessing as mp
+import torch.distributed.rpc as rpc
+from torch import autograd
+from torch.optim import SGD, Adam, RMSprop, Optimizer
+from colorama import Back, Style
+
+from colossalai.pipeline.rpc.PipelineBase import FillDrainPipelineEngine, OneFOneBPipelineEngine
+from colossalai.testing import assert_close
+
+
+def color_debug(text, prefix=' ', color='blue'):
+    color = color.upper()
+    print(getattr(Back, color), prefix, Style.RESET_ALL, text)
+
+
+class RpcTestModel(nn.Module):
+
+    def __init__(self, stage_id, actual_stage_num, feat_num, h) -> None:
+        super().__init__()
+        self.rank = stage_id
+        self.is_last_rank = stage_id == actual_stage_num - 1
+        self.linear_name = f'linear_{stage_id}'
+        if stage_id == 0:
+            setattr(self, self.linear_name, nn.Linear(feat_num, h))
+        elif stage_id == actual_stage_num - 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 parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--world_size', type=int, default=2)
+    parser.add_argument('--num_microbatches', type=int, default=2)
+    parser.add_argument('--chunk', type=int, default=1)
+    parser.add_argument('--use_checkpoint', action='store_true')
+    parser.add_argument('--use_interleave', action='store_true')
+    parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'RMSprop'], default='SGD')
+    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()
+
+
+def run_worker(rank, args, master_func):
+    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:
+        master_func(args)
+    # barrier here
+    rpc.shutdown()
+
+
+def rpc_run(args, master_func):
+    world_size = args.world_size
+    assert args.num_microbatches >= args.world_size, "num_microbatches cannot be fewer than world_size!"
+    mp.spawn(run_worker, args=(args, master_func), nprocs=world_size)