[pipeline] update shardformer policy

colossalai/shardformer/policies/basepolicy.py

@@ -2,9 +2,13 @@
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import Any, Callable, Dict, List, Type, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch.nn as nn
+from torch import Tensor
+from torch.nn import Module
+
+from colossalai.pipeline.stage_manager import PipelineStageManager
 
 from ..shard.shard_config import ShardConfig
 
@@ -71,9 +75,8 @@ class Policy(ABC):
     """
 
     def __init__(self) -> None:
-        self.shard_config = None
-        self.model = None
-        self.shard_config = None
+        self.shard_config: Optional[ShardConfig] = None
+        self.model: Optional[Module] = None
 
     def set_model(self, model: nn.Module) -> None:
         r"""
@@ -94,6 +97,12 @@ class Policy(ABC):
         self.shard_config = shard_config
         self.config_sanity_check()
 
+    @property
+    def pipeline_stage_manager(self) -> Optional[PipelineStageManager]:
+        if self.shard_config is not None:
+            return self.shard_config.pipeline_stage_manager
+        return None
+
     @abstractmethod
     def config_sanity_check(self):
         """
@@ -151,3 +160,19 @@ class Policy(ABC):
             policy[target_key] = ModulePolicyDescription(sub_module_replacement=description)
 
         return policy
+
+    def get_held_layers(self) -> List[Module]:
+        """Get layers that should be held in current stage. This method should be implemented by subclass.
+
+        Returns:
+            List[Module]: List of layers that should be hold in current stage
+        """
+        raise NotImplementedError
+
+    def get_shared_params(self) -> List[Dict[int, Tensor]]:
+        """Get parameters that should be shared across stages. This method should be implemented by subclass.
+
+        Returns:
+            List[Dict[int, Tensor]]: List of parameters that should be shared across stages. E.g. [{0: module.model.embed_tokens.weight, 3: module.lm_head.weight}]
+        """
+        return []

colossalai/shardformer/shard/shard_config.py

@@ -1,8 +1,11 @@
 from dataclasses import dataclass
+from typing import Optional
 
 import torch.distributed as dist
 from torch.distributed import ProcessGroup
 
+from colossalai.pipeline.stage_manager import PipelineStageManager
+
 __all__ = ['ShardConfig']
 
 
@@ -13,11 +16,13 @@ class ShardConfig:
 
     Args:
         tensor_parallel_process_group (int): The process group for tensor parallelism, defaults to None, which is the global process group.
+        pipeline_stage_manager (PipelineStageManager): The pipeline stage manager, defaults to None, which means no pipeline.
         enable_tensor_parallelism (bool): Whether to turn on tensor parallelism, default is True.
         enable_fused_normalization (bool): Whether to use fused layernorm, default is False.
         enable_all_optimization (bool): Whether to turn on all optimization, default is False.
     """
-    tensor_parallel_process_group: ProcessGroup = None
+    tensor_parallel_process_group: Optional[ProcessGroup] = None
+    pipeline_stage_manager: Optional[PipelineStageManager] = None
     enable_tensor_parallelism: bool = True
     enable_fused_normalization: bool = False
     enable_all_optimization: bool = False

colossalai/shardformer/shard/sharder.py

@@ -1,11 +1,15 @@
 from typing import Any, Callable, Dict, List, Union
 
 import torch.nn as nn
+from torch import Tensor
+
+from colossalai.lazy import LazyTensor
 
 from .._utils import getattr_, setattr_
 from ..policies.autopolicy import get_autopolicy
 from ..policies.basepolicy import Policy, SubModuleReplacementDescription
 from .shard_config import ShardConfig
+from .utils import set_tensors_to_none
 
 __all__ = ['ModelSharder', 'shard_model']
 
@@ -25,15 +29,18 @@ class ModelSharder(object):
         self.policy = get_autopolicy(self.model) if policy is None else policy
         self.shard_config = shard_config
 
-    def shard(self) -> None:
+    def shard(self) -> List[Dict[int, Tensor]]:
         r"""
         Shard the model according to the policy
         """
         self.policy.set_model(self.model)
         self.policy.set_shard_config(self.shard_config)
         self._preprocess()
+        self._release_unheld_layers()
         self._replace_module()
+        self._materialize()
         self._postprocess()
+        return self.policy.get_shared_params()
 
     def _preprocess(self) -> None:
         self.model = self.policy.preprocess()
@@ -172,3 +179,23 @@ class ModelSharder(object):
                 )
 
             setattr_(org_layer, suffix, replace_layer)
+
+    def _release_unheld_layers(self) -> None:
+        r"""
+        Release the unheld layers in the model
+        """
+        if self.shard_config and self.shard_config.pipeline_stage_manager:
+            held_layers = self.policy.get_held_layers()
+            set_tensors_to_none(self.model, exclude=set(held_layers))
+
+    def _materialize(self) -> None:
+        r"""
+        Materialize the model if lazy initialization is used
+        """
+        for p in self.model.parameters():
+            if isinstance(p, LazyTensor):
+                p.materialize()
+
+        for b in self.model.buffers():
+            if isinstance(b, LazyTensor):
+                b.materialize()

colossalai/shardformer/shard/shardformer.py

@@ -42,5 +42,5 @@ class ShardFormer:
             policy (`Policy`): the custom policy for sharding
         """
         sharder = ModelSharder(model=model, shard_config=self.shard_config, policy=policy)
-        sharder.shard()
-        return model
+        shared_params = sharder.shard()
+        return model, shared_params

colossalai/shardformer/shard/utils.py

@@ -0,0 +1,19 @@
+from typing import Set
+
+import torch.nn as nn
+
+
+def set_tensors_to_none(model: nn.Module, exclude: Set[nn.Module] = set()) -> None:
+    """Set all parameters and buffers of model to None
+
+    Args:
+        model (nn.Module): The model to set
+    """
+    if model in exclude:
+        return
+    for child in model.children():
+        set_tensors_to_none(child, exclude=exclude)
+    for n, p in model.named_parameters(recurse=False):
+        setattr(model, n, None)
+    for n, buf in model.named_buffers(recurse=False):
+        setattr(model, n, None)