[legacy] move communication and nn to legacy and refactor logger (#4671)

* [legacy] move communication to legacy (#4640)

* [legacy] refactor logger and clean up legacy codes (#4654)

* [legacy] make logger independent to gpc

* [legacy] make optim independent to registry

* [legacy] move test engine to legacy

* [legacy] move nn to legacy (#4656)

* [legacy] move nn to legacy

* [checkpointio] fix save hf config

* [test] remove useledd rpc pp test

* [legacy] fix nn init

* [example] skip tutorial hybriad parallel example

* [devops] test doc check

* [devops] test doc check

tests/test_pipeline/test_cuda_rpc_performance.py

@@ -1,81 +0,0 @@
-import os
-import time
-
-import pytest
-import torch
-import torch.nn as nn
-from rpc_test_utils import parse_args, rpc_run
-from titans.dataloader.cifar10 import build_cifar
-from torchvision.models import resnet50
-from tqdm import tqdm
-
-from colossalai.pipeline.pipelinable import PipelinableContext
-from colossalai.pipeline.rpc import OneFOneBPipelineEngine
-
-
-def flatten(x):
-    return torch.flatten(x, 1)
-
-
-def partition(pp_rank: int, chunk: int, stage_num: int):
-    pipelinable = PipelinableContext()
-
-    # build model partitions
-    with pipelinable:
-        # input : [B, 3, 32, 32]
-        _ = resnet50()
-
-    pipelinable.policy = "customized"
-
-    exec_seq = [
-        'conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'avgpool', (flatten, "behind"), 'fc'
-    ]
-    pipelinable.to_layer_list(exec_seq)
-    partition = pipelinable.partition(chunk, stage_num, pp_rank)
-    return partition
-
-
-def run_master(args):
-    batch_size = args.batch_size
-    chunk = args.chunk
-    device = args.device
-    world_size = args.world_size
-    stage_num = world_size
-    num_microbatches = args.num_microbatches
-
-    # build dataloader
-    root = os.environ.get('DATA', './data')
-    train_dataloader, test_dataloader = build_cifar(batch_size, root, padding=4, crop=32, resize=32)
-    criterion = nn.CrossEntropyLoss()
-
-    pp_engine = OneFOneBPipelineEngine(partition_fn=partition,
-                                       stage_num=stage_num,
-                                       num_microbatches=num_microbatches,
-                                       device=device,
-                                       chunk=chunk,
-                                       criterion=criterion,
-                                       checkpoint=False)
-
-    pp_engine.initialize_optimizer(torch.optim.Adam, lr=1e-3)
-    s = time.time()
-
-    for bx, by in tqdm(train_dataloader):
-        pp_engine.forward_backward(bx, labels=by, forward_only=False)
-
-    cost_time = time.time() - s
-
-    print("total cost time :", cost_time)
-    print("cost time per batch:", cost_time / len(train_dataloader))
-
-
-@pytest.mark.skip("Test for performance, no need for CI")
-def main():
-    args = parse_args()
-    # this is due to limitation of partition function
-    args.world_size = 2
-    args.chunk = 1
-    rpc_run(args, run_master)
-
-
-if __name__ == '__main__':
-    main()