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https://github.com/hpcaitech/ColossalAI.git
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Revert "[sync] sync feature/shardformer with develop"
This commit is contained in:
Frank Lee
GitHub
@@ -1,266 +0,0 @@
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from typing import Any, Callable, Dict, List
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import torch
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import torch.nn as nn
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from transformers.pytorch_utils import Conv1D
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from ..policies.autopolicy import get_autopolicy
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from ..policies.basepolicy import Policy
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from ..utils.utils import getattr_, hasattr_, setattr_
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from .shard_config import ShardConfig
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from .slicer import Slicer
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__all__ = ['ModelSharder', 'shard_model']
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class ModelSharder(object):
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r"""
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Shard the original huggingface model according to the policy
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Args:
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policy (:class:`Policy`): The policy to shard the model
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model (:class:`torch.Module`): The model to shard
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shard_config: The setting of distributed model
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"""
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def __init__(
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self,
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model: nn.Module,
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policy: Policy,
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shard_config: ShardConfig = None, # TODO
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) -> None:
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self.model = model
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self.policy = get_autopolicy(self.model) if policy is None else policy
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self.slicer = Slicer(shard_config)
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self.shard_config = shard_config
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self.model_config = self.model.config
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def shard(self) -> None:
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self.reshape_embedding()
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self.inject_model(self.model)
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self.replace_layer(self.model)
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self.bind_layer(self.model)
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def reshape_embedding(self,) -> None:
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r"""
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Reshape the Embedding layer to make the embedding dimension divisible by world_size
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"""
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vocab_size = self.model_config.vocab_size
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world_size = self.shard_config.world_size
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if vocab_size % world_size != 0:
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new_vocab_size = vocab_size + world_size - vocab_size % world_size
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self.model.resize_token_embeddings(new_vocab_size)
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self.model_config = self.model.config
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def inject_model(
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self,
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model: nn.Module,
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) -> None:
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r"""
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Replace the model to policy defined model
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Mainly modify the forward and backward to fit distributed model
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e.g.
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::
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BertForMaskedLM.forward -> BertForMaskedLM_.forward
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"""
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inject_policy = self.policy.inject_policy()
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if inject_policy is None:
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return
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org_model_cls = inject_policy[0]
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shard_model_cls = inject_policy[1]
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if model.__class__ == org_model_cls:
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for key in shard_model_cls.__dict__.keys():
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if hasattr(model.__class__, key):
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setattr(
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model.__class__,
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key,
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getattr(shard_model_cls, key),
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)
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else:
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raise NotImplementedError(f"{model.__class__} is not implemented so far")
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def replace_layer(
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self,
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model: nn.Module,
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) -> None:
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r"""
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Replace the layer according to the policy, and replace the layer one by one
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Args:
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model (:class:`torch.nn.Module`): The layer to shard
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"""
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argument_policies = self.policy.argument_policy(self.model_config, self.shard_config.world_size)
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for argument_policy in argument_policies.items():
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origin_layer_cls = argument_policy[0]
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attr_dict = argument_policy[1].attr_dict
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param_funcs = argument_policy[1].param_funcs
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self.traverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs)
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def traverse_replace_layer(
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self,
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layer: nn.Module,
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origin_cls: nn.Module,
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attr_dict: Dict[str, Any],
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param_funcs: List[Callable],
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) -> None:
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r"""
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Reverse the replace layer operation
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Args:
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layer (:class:`torch.nn.Module`): The object of layer to shard
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origin_cls (:class:`transformers.model`): The origin layer class
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attr_dict (Dict): The attribute dict to modify
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policy_cls (:class:`Policy`): The policy class
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"""
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if layer.__class__ == origin_cls:
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for k, v in attr_dict.items():
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setattr_(layer, k, v, ignore=True)
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self.shard_one_layer(layer, param_funcs)
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for name, child in layer.named_children():
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self.traverse_replace_layer(child, origin_cls, attr_dict, param_funcs)
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return layer
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def shard_one_layer(
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self,
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org_layer: nn.Module,
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param_funcs: List[Callable],
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) -> None:
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r"""
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Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict
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Args:
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org_layer (:class:`torch.nn.Module`): The origin layer object to shard
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param_funcs (:class:`List[typing.Callable]`): The function list to get shard information in policy class
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"""
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for func in param_funcs:
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policy_layers = func()
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for policy_layer in policy_layers:
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weight = None
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bias = None
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weight_attr = policy_layer.weight
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bias_attr = policy_layer.bias
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replace_layer_cls = policy_layer.replace_layer
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ignore = policy_layer.ignore
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n_cast = policy_layer.n_cast
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reversed = policy_layer.reversed
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if policy_layer.__class__.__name__ == "Col_Layer":
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gather_output = policy_layer.gather_output
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if weight_attr is not None:
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if hasattr_(org_layer, weight_attr):
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weight = getattr_(org_layer, weight_attr)
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elif not ignore:
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raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {weight_attr}")
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if bias_attr is not None:
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if hasattr_(org_layer, bias_attr):
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bias = getattr_(org_layer, bias_attr)
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elif not ignore:
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raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {bias_attr}")
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# dont have the attribute in policy, and ignore is true
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if weight is None and bias is None and ignore:
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continue
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# set the sliced weight and bias to the new nn_col layer
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assert weight is not None or bias is not None
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layer_attr = (lambda x: x[:x.rfind(".")])(weight_attr or bias_attr)
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# slice weight and bias
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weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__, n_cast, reversed)
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# create new object to replace the origin layer
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if replace_layer_cls is not None:
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if isinstance(getattr_(org_layer, layer_attr), (nn.Linear, Conv1D)):
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if replace_layer_cls.__name__ == "Linear1D_Row":
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replace_layer = replace_layer_cls(weight.shape[1],
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weight.shape[0],
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bias=False if bias is None else True)
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elif replace_layer_cls.__name__ == "Linear1D_Col":
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replace_layer = replace_layer_cls(weight.shape[0],
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weight.shape[1],
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bias=False if bias is None else True,
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gather_output=gather_output)
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setattr_(org_layer, layer_attr, replace_layer, ignore=ignore)
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self.set_param(replace_layer, weight, bias)
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elif isinstance(getattr_(org_layer, layer_attr), nn.Embedding):
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replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1],
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getattr_(org_layer, f"{layer_attr}.padding_idx", ignore=True))
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setattr_(org_layer, layer_attr, replace_layer, ignore=ignore)
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self.set_param(replace_layer, weight, bias)
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else:
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raise NotImplementedError(
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f"Replacing {getattr_(org_layer, layer_attr).__class__} is not implemented so far")
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# do not replace the layer object, just replace the weight and bias
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else:
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self.set_param(org_layer, layer_attr, weight, bias)
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def set_param(self,
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layer: Any,
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weight: torch.Tensor = None,
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bias: torch.Tensor = None,
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layer_attr: str = "") -> None:
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r"""
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Reset the weight and bias of the layer object
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Args:
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layer (:class:`torch.nn.Module`): The layer object
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layer_attr (str): The attribute name of the layer
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weight (:class:`torch.Tensor`): The weight of the layer
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bias (:class:`torch.Tensor`): The bias of the layer
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"""
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assert weight is not None or bias is not None
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if weight is not None:
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setattr_(layer, "weight" if layer_attr == "" else layer_attr + ".weight", nn.Parameter(weight.contiguous()))
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self.set_layer_size(layer, layer_attr, weight.shape)
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if bias is not None:
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setattr_(layer, "bias" if layer_attr == "" else layer_attr + ".bias", nn.Parameter(bias.contiguous()))
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def set_layer_size(self, layer: nn.Module, layer_attr: str, size: torch.Size) -> None:
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r"""
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Set the layer attribute
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Args:
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layer (:class:`torch.nn.Module`): The layer object
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layer_attr (str): The attribute name of the layer
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size (:class:`torch.Size`): The size of the tensor
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"""
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# Tensor.shape[0] -> out_features, Tensor.shape[1] -> in_features
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attrs = ["out_features", "in_features"]
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for i, attr in enumerate(attrs):
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if hasattr_(layer, f"{layer_attr}.{attr}"):
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setattr_(layer, f"{layer_attr}.{attr}", size[i])
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def bind_layer(self, model: nn.Module) -> None:
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r"""
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Bind the layer according to the binding policy
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Args:
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model (:class:`torch.nn.Module`): The shard model
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"""
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binding_map = self.policy.binding_policy()
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if binding_map is None:
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return
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for k, v in binding_map.items():
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param = getattr_(model, k)
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param = nn.Parameter(param)
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setattr_(model, k, param)
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setattr_(model, v, param)
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def shard_model(model: nn.Module, shard_config: ShardConfig = None, policy: Policy = None):
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r"""
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The function is used to shard the PyTorch model.
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Args:
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model (`torch.nn.Model`): the origin huggingface model
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shard_config (`ShardConfig`): the config for distribute information
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policy (`Policy`): the custom policy for sharding
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"""
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sharder = ModelSharder(model=model, shard_config=shard_config, policy=policy)
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sharder.shard()
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return model
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