[gemini] gemini supports lazy init

colossalai/_analyzer/_subclasses/_monkey_patch.py

@@ -2,8 +2,6 @@
 import torch.distributed as dist
 from packaging import version
 
-aten = torch.ops.aten
-
 __all__ = [
     "_TorchFactoryMethod",
     "_TorchOverrideableFactoryMethod",
@@ -51,6 +49,7 @@
 ]
 
 if version.parse(torch.__version__) >= version.parse('1.12.0'):
+    aten = torch.ops.aten
     # TODO: dive deep here
     # refer to https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorShape.cpp
     _AliasATen = [

colossalai/booster/plugin/gemini_plugin.py

@@ -16,61 +16,15 @@
 from colossalai.checkpoint_io.utils import save_state_dict
 from colossalai.cluster import DistCoordinator
 from colossalai.interface import ModelWrapper, OptimizerWrapper
-from colossalai.tensor.colo_parameter import ColoParameter
 from colossalai.utils import get_current_device
 from colossalai.zero import GeminiDDP, zero_model_wrapper, zero_optim_wrapper
-from colossalai.zero.gemini.colo_init_context import _convert_to_coloparam
 from colossalai.zero.gemini.memory_tracer import MemStats
 
 from .plugin_base import Plugin
 
 __all__ = ['GeminiPlugin']
 
 
-def convert_to_colo_param(module: nn.Module) -> None:
-    """Convert module's paramters to ColoParameter. This is a workaround and will be deprecated when lazy init is compatible with Gemini.
-
-    Args:
-        module (nn.Module): Module to be converted.
-    """
-    converted_modules = set()    # handle shared modules
-    converted_params = dict()    # record mapping between (torch.Tensor, ColoTensor) to distinguish the same reference
-
-    def convert_recursively(m: nn.Module):
-        for child in m.children():
-            if child not in converted_modules:
-                converted_modules.add(child)
-                convert_recursively(child)
-
-        for name, p in m.named_parameters(recurse=False):
-            assert not isinstance(p, ColoParameter)
-            if p in converted_params:
-                target = converted_params[p]
-            else:
-                target = _convert_to_coloparam(p, p.device, p.dtype)
-                converted_params[p] = target
-            setattr(m, name, target)
-            target.shared_param_modules.append(m)
-
-    convert_recursively(module)
-
-    # optimizer should replace params in group as well. This attr should be deleted after replacing to avoid memory leak
-    module._converted_params = converted_params
-
-
-def replace_param_in_group(optimizer: Optimizer, converted_params: dict) -> None:
-    """Replace param in optimizer's group with converted ColoParameter.
-
-    Args:
-        optimizer (Optimizer): Optimizer to be replaced.
-        converted_params (dict): Mapping between (torch.Tensor, ColoTensor).
-    """
-    for group in optimizer.param_groups:
-        for i, p in enumerate(group['params']):
-            if p in converted_params:
-                group['params'][i] = converted_params[p]
-
-
 class GeminiCheckpointIO(GeneralCheckpointIO):
 
     def __init__(self) -> None:
@@ -113,8 +67,6 @@ class GeminiModel(ModelWrapper):
 
     def __init__(self, module: nn.Module, gemini_config: dict) -> None:
         super().__init__(module)
-        # TODO(ver217): only support Gemini now
-        convert_to_colo_param(module)
         self.module = zero_model_wrapper(module, zero_stage=3, gemini_config=gemini_config)
 
     def unwrap(self):
@@ -125,8 +77,6 @@ def unwrap(self):
 class GeminiOptimizer(OptimizerWrapper):
 
     def __init__(self, module: GeminiDDP, optimizer: Optimizer, zero_optim_config: dict, optim_kwargs: dict) -> None:
-        replace_param_in_group(optimizer, module.module._converted_params)
-        del module.module._converted_params
         optimizer = zero_optim_wrapper(module, optimizer, optim_config=zero_optim_config, **optim_kwargs)
         super().__init__(optimizer)
 

colossalai/nn/optimizer/nvme_optimizer.py

@@ -1,9 +1,10 @@
-import torch
+import math
 import os
 import tempfile
-import math
+from typing import Callable, Dict, List, Optional
+
+import torch
 from torch.nn.parameter import Parameter
-from typing import Optional, List, Dict, Callable
 
 
 class NVMeOptimizer(torch.optim.Optimizer):
@@ -42,8 +43,9 @@ def __init__(self,
             self.offloader = None
         self.is_on_nvme: Dict[Parameter, bool] = {}
         self.offloaded_numel: int = 0
-        self.total_numel: int = self._get_numel()
-        self.can_offload_numel = math.floor(self.total_numel * self.nvme_offload_fraction)
+        # As param may be not materialized here, these attributes are initalized when the first step
+        self.total_numel: Optional[int] = None
+        self.can_offload_numel: Optional[int] = None
 
         self.prefetch_params: List[Parameter] = []
         self.param_to_prefetch_idx: Dict[Parameter, int] = {}
@@ -77,6 +79,9 @@ def _setup_prefetch_params(self) -> List[Parameter]:
                     self.prefetch_params.append(p)
 
     def _pre_step(self, *state_keys: str) -> None:
+        if self.total_numel is None:
+            self.total_numel = self._get_numel()
+            self.can_offload_numel = math.floor(self.total_numel * self.nvme_offload_fraction)
         self._setup_prefetch_params()
         if self.offloader is None or len(self.prefetch_params) == 0:
             return

colossalai/utils/model/experimental.py

@@ -7,7 +7,7 @@
 from torch import Tensor
 from torch.utils._pytree import tree_map
 
-from colossalai.fx.profiler.tensor import MetaTensor
+from colossalai._analyzer._subclasses import MetaTensor
 from colossalai.tensor.d_tensor.d_tensor import DTensor
 from colossalai.tensor.d_tensor.layout import Layout
 
@@ -37,7 +37,7 @@
 # If your intent is to change the metadata of a Tensor (such as sizes / strides / storage / storage_offset)
 # without autograd tracking the change, remove the .data / .detach() call and wrap the change in a `with torch.no_grad():` block.
 # These ops cannot be unwrapped using .data
-_CHANGE_META_OPS = ['_cudnn_rnn_flatten_weight', 'requires_grad_', '__get__']
+_CHANGE_META_OPS = ['_cudnn_rnn_flatten_weight', 'requires_grad_', '__get__', '__set__']
 
 _LEGACY_TENSOR_CONSTRUCTOR = {
     'FloatTensor': torch.float,
@@ -75,6 +75,12 @@ def __torch_function__(cls, func, types, args=(), kwargs=None):
         return super().__torch_function__(func, types, args, kwargs)
 
 
+def _data_tolist(tensor: torch.Tensor) -> list:
+    """tolist() method is not allowed for a subclass of tensor. Tensor.data returns a Tensor.
+    """
+    return tensor.data.tolist()
+
+
 def _convert_cls(tensor: 'LazyTensor', target: torch.Tensor) -> torch.Tensor:
     """Convert a lazy tensor's class to target's class, with target's data.
 
@@ -94,7 +100,7 @@ def _convert_cls(tensor: 'LazyTensor', target: torch.Tensor) -> torch.Tensor:
     tensor.requires_grad = target.requires_grad
     # subclass of torch.Tensor does not have tolist() method
     # overwrite this method after materialization or distribution
-    tensor.tolist = MethodType(torch.Tensor.tolist, target)
+    tensor.tolist = MethodType(_data_tolist, tensor)
     return tensor
 
 
@@ -144,7 +150,7 @@ def __new__(cls, func, *args, meta_data=None, concrete_data=None, **kwargs):
             if meta_data is None:
                 device = kwargs.get('device', 'cpu')
                 elem = func(*args, **{**kwargs, 'device': 'meta'})
-                meta_data = MetaTensor(elem, fake_device=device)
+                meta_data = MetaTensor(elem, device=device)
             elem = meta_data._tensor
         # As a meta tensor cannot be modified __class__ to torch.Tensor, we should use an empty real tensor here
         r = torch.Tensor._make_subclass(cls, _EMPTY_DATA, require_grad=elem.requires_grad)
@@ -255,7 +261,7 @@ def __torch_function__(cls, func, types, args=(), kwargs=None):
             tree_map(cls._replace_with_materialized, args)
             tree_map(cls._replace_with_materialized, kwargs)
         is_inplace: bool = (func.__name__.endswith('_') and not (func.__name__.endswith('__'))
-                            or func.__name__ == "__setitem__")
+                            or func.__name__ in ('__setitem__', '__set__'))
 
         is_change_meta_op: bool = func.__name__ in _CHANGE_META_OPS
 

colossalai/zero/gemini/chunk/search_utils.py

@@ -46,9 +46,10 @@ def _get_unused_byte(size_list: List[int], chunk_size: int) -> int:
 
 
 def _tensor_numel(local_param: ColoParameter, strict_ddp_flag: bool):
-    if strict_ddp_flag:
+    if strict_ddp_flag and type(local_param) is ColoParameter:
         return local_param.numel_global()
     else:
+        # if local_param is not ColoParameter, we assume it's replicated
         return local_param.numel()
 
 
@@ -67,11 +68,13 @@ def classify_params_by_dp_degree(param_order: OrderedParamGenerator,
     """
     params_dict: Dict[int, List[ColoParameter]] = dict()
     for param in param_order.generate():
-        assert isinstance(param, ColoParameter), "please init model in the ColoInitContext"
+        # assert isinstance(param, ColoParameter), "please init model in the ColoInitContext"
         if is_ddp_ignored(param):
             continue
 
-        if strict_ddp_flag:
+        if strict_ddp_flag or type(param) is not ColoParameter:
+            # if model is not initialized with ColoInitContext, we assume it's replicated
+            # TODO(ver217): integrate DTensor
             param_key = dist.get_world_size()
         else:
             param_key = param.process_group.dp_world_size()

colossalai/zero/gemini/gemini_ddp.py

@@ -1,7 +1,7 @@
 import itertools
 from collections import OrderedDict
 from functools import partial
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 import torch
 import torch.distributed as dist
@@ -14,6 +14,7 @@
 from colossalai.tensor.colo_parameter import ColoParameter, ColoTensor, ColoTensorSpec
 from colossalai.tensor.param_op_hook import ColoParamOpHookManager
 from colossalai.utils import get_current_device, is_ddp_ignored
+from colossalai.utils.model.experimental import LazyTensor
 
 from .chunk import Chunk, ChunkManager, TensorState, init_chunk_manager
 from .gemini_hook import GeminiZeROHook
@@ -55,7 +56,6 @@ def __init__(self,
                  pin_memory: bool = False,
                  force_outputs_fp32: bool = False,
                  strict_ddp_mode: bool = False) -> None:
-        super().__init__(module, process_group=ColoProcessGroup())
         self.gemini_manager = gemini_manager
         self.chunk_manager: ChunkManager = gemini_manager.chunk_manager
         self.force_outputs_fp32 = force_outputs_fp32
@@ -67,7 +67,6 @@ def __init__(self,
         self.param2name: Dict[nn.Parameter, str] = dict()
         self.name2param: Dict[str, nn.Parameter] = dict()
 
-        self._cast_buffers()
         self._logger = get_dist_logger()
 
         if self.gemini_manager._premade_memstats_:
@@ -91,6 +90,8 @@ def __init__(self,
             for p_name, p_var in m_var.named_parameters(recurse=False):
                 param_name = m_name + '.' + p_name if m_name else p_name
                 self.name2param[param_name] = p_var
+        super().__init__(module, process_group=ColoProcessGroup())
+        self._cast_buffers()
 
     def _post_forward(self):
         """This function is only triggered for inference.
@@ -478,7 +479,8 @@ def load_fp32_parameter(chunk_slice, data):
     def _init_chunks(self, param_order, strict_ddp_mode: bool, cpu_offload: bool, pin_memory: bool):
         ddp_pg = ColoProcessGroup()
         for p in param_order.generate():
-            assert isinstance(p, ColoParameter)
+            self._preprocess_param(p)
+            assert type(p) is ColoParameter
 
             # gather sharded parameters in the strict ddp mode
             if strict_ddp_mode:
@@ -531,10 +533,27 @@ def _init_chunks(self, param_order, strict_ddp_mode: bool, cpu_offload: bool, pi
 
     def _cast_buffers(self):
         for buffer in self.module.buffers():
+            if isinstance(buffer, LazyTensor):
+                buffer.materialize()
             buffer.data = buffer.cuda()
             if torch.is_floating_point(buffer):
                 buffer.data = buffer.half()
 
+    def _preprocess_param(self, p: Union[nn.Parameter, ColoParameter, 'LazyTensor']) -> None:
+        """Convert parameter to ColoParameter in-place.
+        Args:
+            p (Union[nn.Parameter, ColoParameter, LazyTensor]): parameter to be converted
+        """
+        if type(p) is ColoParameter:
+            # model is initialized with ColoInitContext
+            return
+        requires_grad = p.requires_grad
+        if isinstance(p, LazyTensor):
+            # model is initialized with LazyInitContext
+            p.materialize()
+        p.__class__ = ColoParameter
+        p.__init__(p, requires_grad=requires_grad)
+
 
 class GeminiDDP(ZeroDDP):
 

tests/test_booster/test_plugin/test_gemini_plugin.py

@@ -1,21 +1,31 @@
+from contextlib import nullcontext
+
 import torch
 import torch.distributed as dist
 
 import colossalai
 from colossalai.booster import Booster
 from colossalai.booster.plugin import GeminiPlugin
+from colossalai.fx import is_compatible_with_meta
 from colossalai.nn.optimizer import HybridAdam
 from colossalai.tensor.colo_parameter import ColoParameter
-from colossalai.testing import rerun_if_address_is_in_use, spawn
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.zero import ColoInitContext
 from tests.kit.model_zoo import model_zoo
 
 
-def check_gemini_plugin(early_stop: bool = True):
+@parameterize('init_method', ['lazy', 'none', 'colo'])
+def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True):
     """check gemini plugin over model zoo
 
     Args:
         early_stop (bool, optional): Whether to stop when getting the first error. Defaults to True.
     """
+    is_support_meta = is_compatible_with_meta()
+    if not is_support_meta and init_method == 'lazy':
+        return
+
+    from colossalai.utils.model.experimental import LazyInitContext
     passed_models = []
     failed_info = {}    # (model_name, error) pair
 
@@ -40,10 +50,25 @@ def check_gemini_plugin(early_stop: bool = True):
         ]:
             continue
 
+        if init_method == 'lazy' and name in [
+                'timm_convmixer', 'timm_vision_transformer', 'timm_deit', 'timm_deit3', 'timm_inception_v3',
+                'timm_tnt_b_patch16_224', 'timm_rexnet', 'torchvision_densenet121', 'torchvision_efficientnet_b0',
+                'torchvision_mobilenet_v2', 'torchvision_mnasnet0_5', 'torchvision_regnet_x_16gf',
+                'torchvision_shufflenet_v2_x0_5', 'torchvision_efficientnet_v2_s'
+        ]:
+            continue
+
         try:
+            if init_method == 'colo':
+                ctx = ColoInitContext()
+            elif init_method == 'lazy':
+                ctx = LazyInitContext()
+            else:
+                ctx = nullcontext()
             plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, max_norm=1.0, initial_scale=2**5)
             booster = Booster(plugin=plugin)
-            model = model_fn()
+            with ctx:
+                model = model_fn()
             optimizer = HybridAdam(model.parameters(), lr=1e-3)
             criterion = lambda x: x.mean()
             data = data_gen_fn()
@@ -76,6 +101,7 @@ def check_gemini_plugin(early_stop: bool = True):
         torch.cuda.empty_cache()
 
     if dist.get_rank() == 0:
+        print(f'Init method: {init_method}')
         print(f'Passed models({len(passed_models)}): {passed_models}\n\n')
         print(f'Failed models({len(failed_info)}): {list(failed_info.keys())}\n\n')
     assert len(failed_info) == 0, '\n'.join([f'{k}: {v}' for k, v in failed_info.items()])