[gemini] gemini mgr supports "cpu" placement policy (#1118)

* update gemini mgr

* update chunk

* add docstr

* polish placement policy

* update test chunk

* update test zero

* polish unit test

* remove useless unit test

tests/test_tensor/test_chunk.py

@@ -44,6 +44,7 @@ def run_chunk_zero(use_chunk, use_zero):
     params = [torch.rand(8, 8) for _ in range(3)]
     chunk_size = 128 if use_chunk else None
     chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero)
+    chunk_manager.create_group('param')
     assert chunk_manager.total_mem['cpu'] == 0
     assert chunk_manager.total_mem['cuda'] == 0
     for p in params:

tests/test_tensor/test_zero.py

@@ -1,82 +0,0 @@
-import pytest
-import colossalai
-from colossalai.context.parallel_mode import ParallelMode
-import torch.multiprocessing as mp
-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.model.colo_init_context import ColoInitContext
-from colossalai.tensor import ChunkManager
-from colossalai.core import global_context as gpc
-from functools import partial
-from _utils import tensor_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.testing import parameterize
-from colossalai.gemini.gemini_mgr import GeminiManager
-
-
-def check_param_equal(model, torch_model):
-    for p, torch_p in zip(model.parameters(), torch_model.parameters()):
-        if p.storage().size() > 0:
-            assert tensor_equal(torch_p, p.float()), f'{torch_p} vs {p}'
-
-
-def check_grad_equal(model, torch_model):
-    for p, torch_p in zip(model.parameters(), torch_model.parameters()):
-        if p.grad is not None:
-            assert tensor_equal(torch_p.grad, p.grad.float())
-
-
-@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(checkpoint=True)
-    model = model.cuda()
-    torch_model = model_builder().cuda()
-    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
-        torch_p.data.copy_(p)
-    model = model.half()
-    chunk_size = 38 * 1024**2 if use_chunk else None
-    chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero)
-    gemini_manager = GeminiManager('cuda', chunk_manager)
-    model = ColoDDPV2(model, gemini_manager)
-    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):
-        logits = model(input_ids, attn_mask)
-        torch_logits = torch_model(input_ids, attn_mask)
-        assert tensor_equal(torch_logits, logits.float())
-        loss = criterion(logits, input_ids)
-        torch_loss = criterion(torch_logits, input_ids)
-        model.backward(loss)
-        torch_loss.backward()
-        check_grad_equal(model, torch_model)
-        break
-
-
-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)

tests/test_tensor/test_zero_optim.py

@@ -25,22 +25,28 @@ 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}'
+            assert tensor_equal(torch_p.to(dtype=p.dtype, device=p.device), p), f'{torch_p} vs {p}'
 
 
-def run_step(model, criterion, optimizer, input_ids, attn_mask):
+def check_grad_equal(model, torch_model):
+    for p, torch_p in zip(model.parameters(), torch_model.parameters()):
+        if p.grad is not None:
+            assert tensor_equal(torch_p.grad.to(dtype=p.grad.dtype, device=p.grad.device), p.grad)
+
+
+def run_fwd_bwd(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):
+@parameterize('placement_policy', ['cuda', 'cpu'])
+def run_gpt(use_chunk, use_zero, placement_policy):
     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()
@@ -52,9 +58,11 @@ def run_gpt(use_chunk, use_zero):
     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)
-    gemini_manager = GeminiManager('cuda', chunk_manager)
+    chunk_size = ChunkManager.search_chunk_size(model, 8192, 8) if use_chunk else None
+    chunk_manager = ChunkManager(chunk_size,
+                                 enable_distributed_storage=use_zero,
+                                 init_device=GeminiManager.get_default_device(placement_policy))
+    gemini_manager = GeminiManager(placement_policy, chunk_manager)
     model = ColoDDPV2(model, gemini_manager)
     optim = HybridAdam(model.parameters(), lr=1e-3)
     optim = ZeroOptimizer(optim, model, initial_scale=32)
@@ -64,7 +72,7 @@ def run_gpt(use_chunk, use_zero):
     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)
+    print(chunk_manager)
     check_param_equal(model, torch_model)
     model.train()
     torch_model.train()
@@ -72,9 +80,12 @@ def run_gpt(use_chunk, use_zero):
     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)
+        logits = run_fwd_bwd(model, criterion, optim, input_ids, attn_mask)
+        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
         assert tensor_equal(logits, torch_logits)
+        check_grad_equal(model, torch_model)
+        optim.step()
+        torch_optim.step()
         check_param_equal(model, torch_model)