[test] reorganize zero/gemini tests (#3445)

tests/test_zero/test_gemini/test_zerooptim_state_dict.py

@@ -0,0 +1,91 @@
+from functools import partial
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+import colossalai
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.testing import parameterize, rerun_if_address_is_in_use
+from colossalai.utils import free_port
+from colossalai.utils.cuda import get_current_device
+from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer
+from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration
+from colossalai.zero.gemini.gemini_mgr import GeminiManager
+from tests.components_to_test.registry import non_distributed_component_funcs
+from tests.test_tensor.common_utils import debug_print, set_seed
+
+
+@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
+@parameterize('keep_gathered', [True, False])
+def exam_zero_optim_state_dict(placement_policy, keep_gathered):
+    set_seed(431)
+    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()
+
+    set_seed(451)
+    torch_model = model_builder()    # get a different model
+
+    world_size = torch.distributed.get_world_size()
+    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict[world_size]['chunk_size'] = 5000
+    config_dict[world_size]['keep_gathered'] = keep_gathered
+
+    if placement_policy != 'cuda':
+        init_device = torch.device('cpu')
+    else:
+        init_device = None
+    chunk_manager = ChunkManager(config_dict, init_device=init_device)
+    gemini_manager = GeminiManager(placement_policy, chunk_manager)
+    model = ZeroDDP(model, gemini_manager, pin_memory=True)
+
+    optimizer = HybridAdam(model.parameters())
+    optim = ZeroOptimizer(optimizer, model, initial_scale=32)    # initialize the link between chunk16 and chunk32
+
+    set_seed(dist.get_rank() * 3 + 128)
+    model.train()
+    for i, (input_ids, label) in enumerate(train_dataloader):
+        if i > 0:
+            break
+        optim.zero_grad()
+        logits = model(input_ids)
+        logits = logits.float()
+        loss = criterion(logits, input_ids)
+        optim.backward(loss)
+        optim.step()
+
+    optim_state_dict = optim.state_dict()
+    optim.load_state_dict(optim_state_dict)
+    new_state = optim.state_dict()['state']
+    org_state = optim_state_dict['state']
+
+    for k, v in org_state.items():
+        w = new_state[k]
+        for n, m in v.items():
+            if isinstance(m, torch.Tensor):
+                o = w[n]
+                assert torch.equal(m, o)
+            else:
+                assert m == w[n]
+
+
+def run_dist(rank, world_size, port):
+    config = {}
+    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    exam_zero_optim_state_dict()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_zero_optim(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_zero_optim(1)