[zero] add zero optimizer for ColoTensor (#1046)

* add zero optimizer

* torch ok

* unit test ok

* polish code

* fix bugs

* polish unit test

* polish zero optim

* polish colo ddp v2

* refactor folder structure

* add comment

* polish unit test

* polish zero optim

* polish unit test

tests/test_tensor/test_zero_optim.py

@@ -0,0 +1,93 @@
+import pytest
+import colossalai
+import torch
+import torch.multiprocessing as mp
+from colossalai.context.parallel_mode import ParallelMode
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils.cuda import get_current_device
+from colossalai.utils import free_port
+from colossalai.utils import ColoInitContext
+from colossalai.tensor import ChunkManager
+from colossalai.core import global_context as gpc
+from functools import partial
+from _utils import tensor_equal, tensor_shard_equal, set_seed
+from tests.components_to_test.registry import non_distributed_component_funcs
+from torch.nn.parallel import DistributedDataParallel as DDP
+from colossalai.nn.parallel import ColoDDPV2
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.zero import ZeroOptimizer
+from colossalai.testing import parameterize
+from colossalai.amp import convert_to_apex_amp
+
+
+def check_param_equal(model, torch_model):
+    for p, torch_p in zip(model.parameters(), torch_model.parameters()):
+        if p.storage().size() > 0:
+            assert p.dtype == torch.half
+            assert tensor_equal(torch_p, p), f'{torch_p} vs {p}'
+
+
+def run_step(model, criterion, optimizer, input_ids, attn_mask):
+    optimizer.zero_grad()
+    logits = model(input_ids, attn_mask)
+    logits = logits.float()
+    loss = criterion(logits, input_ids)
+    optimizer.backward(loss)
+    optimizer.step()
+    return logits
+
+
+@parameterize('use_chunk', [False, True])
+@parameterize('use_zero', [False, True])
+def run_gpt(use_chunk, use_zero):
+    set_seed(42)
+    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
+    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder()
+    model = model.cuda().half()
+    torch_model = model_builder().cuda()
+    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
+        torch_p.data.copy_(p)
+
+    chunk_size = 38 * 1024**2 if use_chunk else None
+    chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero)
+    model = ColoDDPV2(model, chunk_manager)
+    optim = HybridAdam(model.parameters(), lr=1e-3)
+    optim = ZeroOptimizer(optim, model, initial_scale=32)
+
+    amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=32)
+    torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
+    torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
+    torch_model = DDP(torch_model, device_ids=[gpc.get_global_rank()], process_group=gpc.get_group(ParallelMode.DATA))
+
+    # print(chunk_manager)
+    check_param_equal(model, torch_model)
+    model.train()
+    torch_model.train()
+    set_seed(gpc.get_local_rank(ParallelMode.DATA))
+    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+        if i > 2:
+            break
+        logits = run_step(model, criterion, optim, input_ids, attn_mask)
+        torch_logits = run_step(torch_model, criterion, torch_optim, input_ids, attn_mask)
+        assert tensor_equal(logits, torch_logits)
+        check_param_equal(model, torch_model)
+
+
+def run_dist(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run_gpt()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_gpt(world_size):
+    run_func = partial(run_dist, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_gpt(4)