[zero] add chunk init function for users (#1729)

* add chunk manager init function

* fix unit tests

* add comment

* add flush=True

colossalai/gemini/chunk/__init__.py

-from .chunk import TensorState, TensorInfo, ChunkFullError, Chunk
-from .manager import ChunkManager
-from .search_utils import clasify_params, search_chunk_configuration
+from .chunk import Chunk, ChunkFullError, TensorInfo, TensorState
+from .manager import ChunkManager
+from .search_utils import clasify_params, search_chunk_configuration
+from .utils import init_chunk_manager

colossalai/gemini/chunk/search_utils.py

-import math
-from typing import Dict, List
-import numpy as np
-import torch.nn as nn
-from colossalai.tensor import ColoParameter
-
-
-def _filter_exlarge_params(model: nn.Module, size_dict: Dict[int, List[int]]) -> None:
-    """Filter those parameters whose size is too large from others.
-    """
-    params_size = [p.numel() for p in model.parameters() if not getattr(p, '_ddp_to_ignore', False)]
-    params_size_arr = np.array(params_size)
-
-    std = np.std(params_size_arr)
-    mean = np.mean(params_size_arr)
-    upper_limit = mean + 3 * std
-
-    for key in size_dict:
-        org_list = size_dict[key]
-        size_dict[key] = list(filter(lambda x: x <= upper_limit, org_list))
-
-
-def _get_unused_byte(size_list: List[int], chunk_size: int) -> int:
-    """Get unused byte for a certain chunk size.
-    """
-    acc = 0
-    left = 0
-    for s in size_list:
-        if s > left:
-            acc += left
-            left = chunk_size
-        left -= s
-    return left + acc
-
-
-def clasify_params(model: nn.Module) -> Dict[int, List[ColoParameter]]:
-    params_dict: Dict[int, List[ColoParameter]] = dict()
-    for param in model.parameters():
-        assert isinstance(param, ColoParameter), "please init model in the ColoInitContext"
-        if getattr(param, '_ddp_to_ignore', False):
-            continue
-
-        param_key = param.process_group.dp_world_size()
-
-        if param_key not in params_dict:
-            params_dict[param_key] = []
-        params_dict[param_key].append(param)
-
-    return params_dict
-
-
-def search_chunk_configuration(
-        model: nn.Module,
-        search_range_mb: float,
-        search_interval_byte: int,    # hidden size is the best value for the interval
-        min_chunk_size_mb: float = 32,
-        filter_exlarge_params: bool = True) -> Dict:
-    search_range_byte = round(search_range_mb * 1024**2)
-    min_chunk_size_byte = round(min_chunk_size_mb * 1024**2)
-    assert search_range_byte >= 0
-
-    params_dict = clasify_params(model)
-    config_dict: Dict[int, Dict] = dict()
-
-    size_dict: Dict[int, List[int]] = dict()
-    for key in params_dict:
-        params_list = params_dict[key]
-        size_list = [p.numel() for p in params_list]
-        # let small parameters keep gathered in CUDA all the time
-        total_size = sum(size_list)
-        if total_size < min_chunk_size_byte:
-            config_dict[key] = dict(chunk_size=total_size, keep_gathered=True)
-        else:
-            size_dict[key] = size_list
-
-    if filter_exlarge_params:
-        _filter_exlarge_params(model, size_dict)
-
-    max_size = min_chunk_size_byte
-    for key in size_dict:
-        max_size = max(max_size, max(size_dict[key]))
-    start_size = int(math.ceil(max_size / search_interval_byte) * search_interval_byte)
-
-    min_chunk_waste = float('+inf')
-    best_chunk_size = start_size
-
-    for chunk_size in range(start_size, start_size + search_range_byte + 1, search_interval_byte):
-        temp_waste = 0
-        for key in size_dict:
-            temp_waste += _get_unused_byte(size_dict[key], chunk_size)
-        if temp_waste < min_chunk_waste:
-            min_chunk_waste = temp_waste
-            best_chunk_size = chunk_size
-
-    for key in params_dict:
-        if key in config_dict:
-            continue
-        config_dict[key] = dict(chunk_size=best_chunk_size, keep_gathered=False)
-
-    return config_dict
+import math
+from typing import Dict, List, Tuple
+
+import numpy as np
+import torch.nn as nn
+
+from colossalai.tensor import ColoParameter
+
+
+def in_ddp(param: nn.Parameter) -> bool:
+    return not getattr(param, '_ddp_to_ignore', False)
+
+
+def _filter_exlarge_params(model: nn.Module, size_dict: Dict[int, List[int]]) -> None:
+    """Filter those parameters whose size is too large from others.
+    """
+    params_size = [p.numel() for p in model.parameters() if in_ddp(p)]
+    params_size_arr = np.array(params_size)
+
+    std = np.std(params_size_arr)
+    mean = np.mean(params_size_arr)
+    upper_limit = mean + 3 * std
+
+    for key in size_dict:
+        org_list = size_dict[key]
+        size_dict[key] = list(filter(lambda x: x <= upper_limit, org_list))
+
+
+def _get_unused_byte(size_list: List[int], chunk_size: int) -> int:
+    """Get unused byte for a certain chunk size.
+    """
+    acc = 0
+    left = 0
+    for s in size_list:
+        if s > left:
+            acc += left
+            left = chunk_size
+        left -= s
+    return left + acc
+
+
+def clasify_params(model: nn.Module) -> Dict[int, List[ColoParameter]]:
+    """Clasify each parameter by its size of DP group.
+    """
+    params_dict: Dict[int, List[ColoParameter]] = dict()
+    for param in model.parameters():
+        assert isinstance(param, ColoParameter), "please init model in the ColoInitContext"
+        if not in_ddp(param):
+            continue
+
+        param_key = param.process_group.dp_world_size()
+
+        if param_key not in params_dict:
+            params_dict[param_key] = []
+        params_dict[param_key].append(param)
+
+    return params_dict
+
+
+def search_chunk_configuration(
+        model: nn.Module,
+        search_range_mb: float,
+        search_interval_byte: int,    # hidden size is the best value for the interval
+        min_chunk_size_mb: float = 32,
+        filter_exlarge_params: bool = True) -> Tuple[Dict, int]:
+    search_range_byte = round(search_range_mb * 1024**2)
+    min_chunk_size_byte = round(min_chunk_size_mb * 1024**2)
+    assert search_range_byte >= 0
+
+    params_dict = clasify_params(model)
+    config_dict: Dict[int, Dict] = dict()
+
+    size_dict: Dict[int, List[int]] = dict()
+    for key in params_dict:
+        params_list = params_dict[key]
+        size_list = [p.numel() for p in params_list]
+        # let small parameters keep gathered in CUDA all the time
+        total_size = sum(size_list)
+        if total_size < min_chunk_size_byte:
+            config_dict[key] = dict(chunk_size=total_size, keep_gathered=True)
+        else:
+            size_dict[key] = size_list
+
+    if filter_exlarge_params:
+        _filter_exlarge_params(model, size_dict)
+
+    max_size = min_chunk_size_byte
+    for key in size_dict:
+        max_size = max(max_size, max(size_dict[key]))
+    start_size = int(math.ceil(max_size / search_interval_byte) * search_interval_byte)
+
+    min_chunk_waste = float('+inf')
+    best_chunk_size = start_size
+
+    for chunk_size in range(start_size, start_size + search_range_byte + 1, search_interval_byte):
+        temp_waste = 0
+        for key in size_dict:
+            temp_waste += _get_unused_byte(size_dict[key], chunk_size)
+        if temp_waste < min_chunk_waste:
+            min_chunk_waste = temp_waste
+            best_chunk_size = chunk_size
+
+    for key in params_dict:
+        if key in config_dict:
+            continue
+        config_dict[key] = dict(chunk_size=best_chunk_size, keep_gathered=False)
+
+    return config_dict, min_chunk_waste

colossalai/gemini/chunk/utils.py

+from time import time
+from typing import Optional
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+
+from colossalai.gemini.chunk import ChunkManager
+from colossalai.gemini.chunk.search_utils import in_ddp, search_chunk_configuration
+
+
+def init_chunk_manager(model: nn.Module,
+                       init_device: Optional[torch.device] = None,
+                       hidden_dim: Optional[int] = None,
+                       search_range_mb: Optional[float] = None,
+                       min_chunk_size_mb: Optional[float] = None,
+                       filter_exlarge_params: Optional[bool] = None) -> ChunkManager:
+
+    kwargs_dict = dict()
+
+    if hidden_dim:
+        search_interval_byte = hidden_dim
+    else:
+        search_interval_byte = 1024    # 1kb
+    kwargs_dict["search_interval_byte"] = search_interval_byte
+
+    if search_range_mb:
+        kwargs_dict["search_range_mb"] = search_range_mb
+
+    if min_chunk_size_mb:
+        kwargs_dict["min_chunk_size_mb"] = min_chunk_size_mb
+
+    if filter_exlarge_params:
+        kwargs_dict["filter_exlarge_params"] = filter_exlarge_params
+
+    params_sizes = [p.numel() for p in model.parameters() if in_ddp(p)]
+    total_size = sum(params_sizes) / 1024**2
+
+    dist.barrier()
+    begine = time()
+
+    config_dict, wasted_size = search_chunk_configuration(model, **kwargs_dict)
+
+    dist.barrier()
+    end = time()
+    span_s = end - begine
+    wasted_size /= 1024**2
+
+    if dist.get_rank() == 0:
+        print("searching chunk configuration is completed in {:.2f} s.\n".format(span_s),
+              "used number: {:.2f} MB, wasted number: {:.2f} MB\n".format(total_size, wasted_size),
+              "total wasted percentage is {:.2f}%".format(100 * wasted_size / (total_size + wasted_size)),
+              sep='',
+              flush=True)
+    dist.barrier()
+
+    chunk_manager = ChunkManager(config_dict, init_device)
+    return chunk_manager

tests/test_ddp/test_ddp_ignore_params.py

+import os
+import random
+from functools import partial
+from typing import Callable, Type
+
+import numpy as np
 import pytest
-import colossalai
 import torch
+import torch.distributed as dist
 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
+
+import colossalai
 from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
-from functools import partial
-from colossalai.nn.parallel import ColoDDP, ZeroDDP
 from colossalai.gemini.gemini_mgr import GeminiManager
-from typing import Callable, Type
-import torch.distributed as dist
-import os
-import random
-import numpy as np
+from colossalai.nn.parallel import ColoDDP, ZeroDDP
 from colossalai.tensor import ProcessGroup
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils import free_port
+from colossalai.utils.cuda import get_current_device
+from colossalai.utils.model.colo_init_context import ColoInitContext
 
 
 def set_seed(seed):
@@ -33,7 +35,7 @@ def init_ddp(module: torch.nn.Module) -> ColoDDP:
 
 
 def init_ddpv2(module: torch.nn.Module) -> ZeroDDP:
-    chunk_config = search_chunk_configuration(module, 4, 1024)
+    chunk_config, _ = search_chunk_configuration(module, 4, 1024)
     chunk_manager = ChunkManager(chunk_config)
     gemini_manager = GeminiManager('cuda', chunk_manager)
     return ZeroDDP(module, gemini_manager)

tests/test_gemini/update/test_fwd_bwd.py

-import pytest
-import colossalai
-import torch
-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 functools import partial
-from tests.test_tensor.common_utils import tensor_equal, set_seed, tensor_shard_equal
-from tests.components_to_test.registry import non_distributed_component_funcs
-from torch.nn.parallel import DistributedDataParallel as DDP
-from colossalai.gemini.chunk import search_chunk_configuration, ChunkManager
-from colossalai.nn.parallel import ZeroDDP
-from colossalai.testing import parameterize
-from colossalai.amp import convert_to_apex_amp
-from colossalai.gemini.gemini_mgr import GeminiManager
-from colossalai.tensor import ProcessGroup
-from tests.test_tensor.common_utils import debug_print
-
-
-def check_grad(model: ZeroDDP, torch_model: torch.nn.Module):
-    chunk_manager = model.chunk_manager
-    param_list = [p for p in model.parameters()]
-    chunk_list = chunk_manager.get_chunks(param_list)
-    for chunk in chunk_list:
-        chunk_manager.access_chunk(chunk)
-
-    for (p0, p1) in zip(model.parameters(), torch_model.parameters()):
-        assert torch.allclose(p0, p1.grad, atol=1e-3, rtol=1e-5), "{}".format(torch.max(torch.abs(p0 - p1.grad)).item())
-
-
-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)
-    return logits
-
-
-@parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
-def exam_gpt_fwd_bwd(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()
-
-    with ColoInitContext(device=get_current_device()):
-        model = model_builder()
-
-    torch_model = model_builder().cuda()
-    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
-        torch_p.data.copy_(p.data)
-
-    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'] = False
-    chunk_manager = ChunkManager(config_dict)
-    gemini_manager = GeminiManager(placement_policy, chunk_manager)
-    model = ZeroDDP(model, gemini_manager, pin_memory=True)
-
-    pg = ProcessGroup()
-    amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
-    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=[pg.rank()], process_group=pg.dp_process_group())
-
-    model.eval()
-    torch_model.eval()
-
-    set_seed(pg.dp_local_rank())
-    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
-        if i > 0:
-            break
-
-        logits = model(input_ids, attn_mask)
-        logits = logits.float()
-        loss = criterion(logits, input_ids)
-        model.backward(loss)
-
-        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
-        assert torch.allclose(logits, torch_logits, rtol=0), "{} {} {}".format(
-            torch.max(torch.abs(logits - torch_logits)).item(), logits, torch_logits)
-
-        check_grad(model, torch_model)
-
-
-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_gpt_fwd_bwd()
-
-
-@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(1)
+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+import colossalai
+from colossalai.amp import convert_to_apex_amp
+from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
+from colossalai.gemini.gemini_mgr import GeminiManager
+from colossalai.nn.parallel import ZeroDDP
+from colossalai.tensor import ProcessGroup
+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.utils.model.colo_init_context import ColoInitContext
+from tests.components_to_test.registry import non_distributed_component_funcs
+from tests.test_tensor.common_utils import debug_print, set_seed, tensor_equal, tensor_shard_equal
+
+
+def check_grad(model: ZeroDDP, torch_model: torch.nn.Module):
+    chunk_manager = model.chunk_manager
+    param_list = [p for p in model.parameters()]
+    chunk_list = chunk_manager.get_chunks(param_list)
+    for chunk in chunk_list:
+        chunk_manager.access_chunk(chunk)
+
+    for (p0, p1) in zip(model.parameters(), torch_model.parameters()):
+        assert torch.allclose(p0, p1.grad, atol=1e-3, rtol=1e-5), "{}".format(torch.max(torch.abs(p0 - p1.grad)).item())
+
+
+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)
+    return logits
+
+
+@parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
+def exam_gpt_fwd_bwd(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()
+
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder()
+
+    torch_model = model_builder().cuda()
+    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
+        torch_p.data.copy_(p.data)
+
+    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'] = False
+    chunk_manager = ChunkManager(config_dict)
+    gemini_manager = GeminiManager(placement_policy, chunk_manager)
+    model = ZeroDDP(model, gemini_manager, pin_memory=True)
+
+    pg = ProcessGroup()
+    amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
+    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=[pg.rank()], process_group=pg.dp_process_group())
+
+    model.eval()
+    torch_model.eval()
+
+    set_seed(pg.dp_local_rank())
+    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+        if i > 0:
+            break
+
+        logits = model(input_ids, attn_mask)
+        logits = logits.float()
+        loss = criterion(logits, input_ids)
+        model.backward(loss)
+
+        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
+        assert torch.allclose(logits, torch_logits, rtol=0), "{} {} {}".format(
+            torch.max(torch.abs(logits - torch_logits)).item(), logits, torch_logits)
+
+        check_grad(model, torch_model)
+
+
+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_gpt_fwd_bwd()
+
+
+@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(1)

tests/test_gemini/update/test_optim.py

-import pytest
-import colossalai
-import torch
-import torch.multiprocessing as mp
-import torch.distributed as dist
-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 functools import partial
-from tests.test_tensor.common_utils import tensor_equal, set_seed, tensor_shard_equal
-from tests.components_to_test.registry import non_distributed_component_funcs
-from torch.nn.parallel import DistributedDataParallel as DDP
-from colossalai.nn.parallel import ZeroDDP
-from colossalai.nn.optimizer import HybridAdam
-from colossalai.zero import ZeroOptimizer
-from colossalai.testing import parameterize
-from colossalai.amp import convert_to_apex_amp
-from colossalai.gemini.gemini_mgr import GeminiManager
-from tests.test_tensor.common_utils import debug_print
-
-from time import time
-from colossalai.gemini.chunk import search_chunk_configuration, ChunkManager
-
-
-def check_param(model: ZeroDDP, torch_model: torch.nn.Module):
-    zero_dict = model.state_dict(only_rank_0=False)
-    torch_dict = torch_model.state_dict()
-
-    for key, value in torch_dict.items():
-        # key is 'module.model.PARAMETER', so we truncate it
-        key = key[7:]
-        if key == 'model.lm_head.weight':
-            continue
-        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
-        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
-        # debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
-        assert torch.allclose(value, temp_zero_value, rtol=1e-3, atol=1e-2), "parameter '{}' has problem.".format(key)
-
-
-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)
-    return logits
-
-
-@parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
-def exam_gpt_fwd_bwd(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()
-
-    with ColoInitContext(device=get_current_device()):
-        model = model_builder()
-
-    torch_model = model_builder().cuda()
-    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
-        torch_p.data.copy_(p.data)
-
-    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'] = False
-    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(), lr=1e-3)
-    zero_optim = ZeroOptimizer(optimizer, model, initial_scale=2)
-
-    amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
-    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=[dist.get_rank()])
-
-    model.eval()
-    torch_model.eval()
-
-    set_seed(dist.get_rank() * 3 + 128)
-    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
-        if i > 2:
-            break
-
-        zero_logits = run_fwd_bwd(model, criterion, zero_optim, input_ids, attn_mask)
-        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
-        assert torch.allclose(zero_logits, torch_logits, rtol=1e-3, atol=1e-2)
-        # debug_print([0], zero_logits, torch_logits)
-
-        zero_optim.step()
-        torch_optim.step()
-
-        check_param(model, torch_model)
-
-
-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_gpt_fwd_bwd()
-
-
-@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(1)
+from functools import partial
+from time import time
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+import colossalai
+from colossalai.amp import convert_to_apex_amp
+from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
+from colossalai.gemini.gemini_mgr import GeminiManager
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.nn.parallel import ZeroDDP
+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.utils.model.colo_init_context import ColoInitContext
+from colossalai.zero import ZeroOptimizer
+from tests.components_to_test.registry import non_distributed_component_funcs
+from tests.test_tensor.common_utils import debug_print, set_seed, tensor_equal, tensor_shard_equal
+
+
+def check_param(model: ZeroDDP, torch_model: torch.nn.Module):
+    zero_dict = model.state_dict(only_rank_0=False)
+    torch_dict = torch_model.state_dict()
+
+    for key, value in torch_dict.items():
+        # key is 'module.model.PARAMETER', so we truncate it
+        key = key[7:]
+        if key == 'model.lm_head.weight':
+            continue
+        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
+        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
+        # debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
+        assert torch.allclose(value, temp_zero_value, rtol=1e-3, atol=1e-2), "parameter '{}' has problem.".format(key)
+
+
+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)
+    return logits
+
+
+@parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
+def exam_gpt_fwd_bwd(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()
+
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder()
+
+    torch_model = model_builder().cuda()
+    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
+        torch_p.data.copy_(p.data)
+
+    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'] = False
+    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(), lr=1e-3)
+    zero_optim = ZeroOptimizer(optimizer, model, initial_scale=2)
+
+    amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
+    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=[dist.get_rank()])
+
+    model.eval()
+    torch_model.eval()
+
+    set_seed(dist.get_rank() * 3 + 128)
+    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+        if i > 2:
+            break
+
+        zero_logits = run_fwd_bwd(model, criterion, zero_optim, input_ids, attn_mask)
+        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
+        assert torch.allclose(zero_logits, torch_logits, rtol=1e-3, atol=1e-2)
+        # debug_print([0], zero_logits, torch_logits)
+
+        zero_optim.step()
+        torch_optim.step()
+
+        check_param(model, torch_model)
+
+
+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_gpt_fwd_bwd()
+
+
+@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(1)

tests/test_gemini/update/test_search.py

-import pytest
-
-from functools import partial
-
-import torch
-import torch.multiprocessing as mp
-import torch.distributed as dist
-
-import colossalai
-from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.gemini.chunk import search_chunk_configuration
-from colossalai.utils import free_port, get_current_device
-from colossalai.utils.model.colo_init_context import ColoInitContext
-from colossalai.tensor import ShardSpec, ComputePattern, ComputeSpec, ProcessGroup
-from tests.components_to_test.registry import non_distributed_component_funcs
-
-
-def init_1d_row_spec(model, pg: ProcessGroup):
-    tensor_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
-    for n, p in model.named_parameters():
-        if 'weight' in n and 'ln' not in n:
-            p.set_process_group(pg)
-            p.set_tensor_spec(*tensor_spec)
-
-
-def exam_search_chunk_size():
-
-    world_size = torch.distributed.get_world_size()
-    pg_tp = ProcessGroup(tp_degree=world_size)
-
-    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
-    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
-
-    # make sure torch_model and model has the same parameter values
-    with ColoInitContext(device=get_current_device()):
-        model = model_builder()
-    init_1d_row_spec(model, pg_tp)
-    config_dict = search_chunk_configuration(model,
-                                             search_range_mb=1,
-                                             search_interval_byte=16,
-                                             min_chunk_size_mb=0,
-                                             filter_exlarge_params=True)
-
-    for key in config_dict:
-        chunk_size = config_dict[key]['chunk_size']
-        if world_size == 1:
-            assert chunk_size == 31616
-        else:
-            assert chunk_size == 1024
-
-
-def run_dist(rank, world_size, port):
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-    exam_search_chunk_size()
-
-
-@pytest.mark.dist
-@pytest.mark.parametrize('world_size', [1, 4])
-@rerun_if_address_is_in_use()
-def test_search(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_search(4)
+from functools import partial
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+import colossalai
+from colossalai.gemini.chunk import search_chunk_configuration
+from colossalai.tensor import ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils import free_port, get_current_device
+from colossalai.utils.model.colo_init_context import ColoInitContext
+from tests.components_to_test.registry import non_distributed_component_funcs
+
+
+def init_1d_row_spec(model, pg: ProcessGroup):
+    tensor_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
+    for n, p in model.named_parameters():
+        if 'weight' in n and 'ln' not in n:
+            p.set_process_group(pg)
+            p.set_tensor_spec(*tensor_spec)
+
+
+def exam_search_chunk_size():
+
+    world_size = torch.distributed.get_world_size()
+    pg_tp = ProcessGroup(tp_degree=world_size)
+
+    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
+    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+
+    # make sure torch_model and model has the same parameter values
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder()
+    init_1d_row_spec(model, pg_tp)
+    config_dict, _ = search_chunk_configuration(model,
+                                                search_range_mb=1,
+                                                search_interval_byte=16,
+                                                min_chunk_size_mb=0,
+                                                filter_exlarge_params=True)
+
+    for key in config_dict:
+        chunk_size = config_dict[key]['chunk_size']
+        if world_size == 1:
+            assert chunk_size == 31616
+        else:
+            assert chunk_size == 1024
+
+
+def run_dist(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    exam_search_chunk_size()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_search(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_search(4)

tests/test_gemini/update/test_zeroddp_state_dict.py

-import pytest
-import colossalai
-import torch
-import torch.multiprocessing as mp
-import torch.distributed as dist
-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 functools import partial
-from tests.test_tensor.common_utils import set_seed
-from tests.components_to_test.registry import non_distributed_component_funcs
-from colossalai.nn.parallel import ZeroDDP
-from colossalai.testing import parameterize
-from colossalai.gemini.gemini_mgr import GeminiManager
-from tests.test_tensor.common_utils import debug_print
-
-from colossalai.gemini.chunk import search_chunk_configuration, ChunkManager
-
-
-@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
-@parameterize('keep_gathered', [True, False])
-def exam_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()
-
-    torch_model = model_builder()
-    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
-        torch_p.data.copy_(p.data)
-
-    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
-    chunk_manager = ChunkManager(config_dict)
-    gemini_manager = GeminiManager(placement_policy, chunk_manager)
-    model = ZeroDDP(model, gemini_manager, pin_memory=True)
-    model.train()
-
-    zero_dict = model.state_dict(only_rank_0=False)
-    torch_dict = torch_model.state_dict()
-
-    for key, value in torch_dict.items():
-        if key == 'model.lm_head.weight':
-            continue
-        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
-        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
-        assert torch.equal(value, temp_zero_value), "parameter '{}' has problem.".format(key)
-
-
-@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
-@parameterize('keep_gathered', [True, False])
-def exam_load_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)
-
-    torch_dict = torch_model.state_dict()
-    model.load_state_dict(torch_dict, strict=False)
-    zero_dict = model.state_dict(only_rank_0=False)
-
-    for key, value in torch_dict.items():
-        if key == 'model.lm_head.weight':
-            continue
-        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
-        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
-        assert torch.equal(value, temp_zero_value), "parameter '{}' has problem.".format(key)
-
-
-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_state_dict()
-    exam_load_state_dict()
-
-
-@pytest.mark.dist
-@pytest.mark.parametrize('world_size', [1, 4])
-@rerun_if_address_is_in_use()
-def test_zero_ddp(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_ddp(1)
+from functools import partial
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+import colossalai
+from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
+from colossalai.gemini.gemini_mgr import GeminiManager
+from colossalai.nn.parallel import ZeroDDP
+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.utils.model.colo_init_context import ColoInitContext
+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_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()
+
+    torch_model = model_builder()
+    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
+        torch_p.data.copy_(p.data)
+
+    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
+    chunk_manager = ChunkManager(config_dict)
+    gemini_manager = GeminiManager(placement_policy, chunk_manager)
+    model = ZeroDDP(model, gemini_manager, pin_memory=True)
+    model.train()
+
+    zero_dict = model.state_dict(only_rank_0=False)
+    torch_dict = torch_model.state_dict()
+
+    for key, value in torch_dict.items():
+        if key == 'model.lm_head.weight':
+            continue
+        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
+        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
+        assert torch.equal(value, temp_zero_value), "parameter '{}' has problem.".format(key)
+
+
+@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
+@parameterize('keep_gathered', [True, False])
+def exam_load_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)
+
+    torch_dict = torch_model.state_dict()
+    model.load_state_dict(torch_dict, strict=False)
+    zero_dict = model.state_dict(only_rank_0=False)
+
+    for key, value in torch_dict.items():
+        if key == 'model.lm_head.weight':
+            continue
+        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
+        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
+        assert torch.equal(value, temp_zero_value), "parameter '{}' has problem.".format(key)
+
+
+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_state_dict()
+    exam_load_state_dict()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_zero_ddp(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_ddp(1)

tests/test_gemini/update/test_zerooptim_state_dict.py

-import pytest
-import colossalai
-import torch
-import torch.multiprocessing as mp
-import torch.distributed as dist
-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 functools import partial
-from tests.test_tensor.common_utils import set_seed
-from tests.components_to_test.registry import non_distributed_component_funcs
-from colossalai.nn.parallel import ZeroDDP
-from colossalai.zero import ZeroOptimizer
-from colossalai.nn.optimizer import HybridAdam
-from colossalai.testing import parameterize
-from colossalai.gemini.gemini_mgr import GeminiManager
-from tests.test_tensor.common_utils import debug_print
-
-from colossalai.gemini.chunk import search_chunk_configuration, ChunkManager
-
-
-@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, attn_mask) in enumerate(train_dataloader):
-        if i > 0:
-            break
-        optim.zero_grad()
-        logits = model(input_ids, attn_mask)
-        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]
-                if m.device != o.device:
-                    o = o.to(m.device)
-                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)
+from functools import partial
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+import colossalai
+from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
+from colossalai.gemini.gemini_mgr import GeminiManager
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.nn.parallel import ZeroDDP
+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.utils.model.colo_init_context import ColoInitContext
+from colossalai.zero import ZeroOptimizer
+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, attn_mask) in enumerate(train_dataloader):
+        if i > 0:
+            break
+        optim.zero_grad()
+        logits = model(input_ids, attn_mask)
+        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]
+                if m.device != o.device:
+                    o = o.to(m.device)
+                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)

tests/test_tensor/test_tp_with_zero.py

+from functools import partial
+
 import pytest
-import colossalai
 import torch
 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.gemini.chunk import ChunkManager, search_chunk_configuration
-from functools import partial
-from tests.test_tensor.common_utils import tensor_equal, set_seed, tensor_shard_equal
-from tests.components_to_test.registry import non_distributed_component_funcs
 from torch.nn.parallel import DistributedDataParallel as DDP
-from colossalai.nn.parallel import ZeroDDP
-from colossalai.nn.optimizer import HybridAdam
-from colossalai.zero import ZeroOptimizer
-from colossalai.testing import parameterize
+
+import colossalai
 from colossalai.amp import convert_to_apex_amp
+from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
 from colossalai.gemini.gemini_mgr import GeminiManager
-from colossalai.tensor import ColoTensorSpec, ShardSpec, ComputePattern, ComputeSpec, ProcessGroup, ColoTensor
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.nn.parallel import ZeroDDP
+from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
+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.utils.model.colo_init_context import ColoInitContext
+from colossalai.zero import ZeroOptimizer
+from tests.components_to_test.registry import non_distributed_component_funcs
+from tests.test_tensor.common_utils import set_seed, tensor_equal, tensor_shard_equal
 from tests.test_tensor.model.test_gpt2 import init_megatron_spec
 
 
@@ -88,7 +89,7 @@ def run_gpt(placement_policy, tp_init_spec_func=None):
         tp_init_spec_func(model, pg)
 
     dp_world_size = pg.dp_world_size()
-    config_dict = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, _ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
     config_dict[dp_world_size]['chunk_size'] = 5000
     config_dict[dp_world_size]['keep_gathered'] = False
     if placement_policy != 'cuda':